SYMBOL INDEX (2220 symbols across 107 files) FILE: .github/workflows/generate_lkg.py function simple_constraint_map (line 31) | def simple_constraint_map(all_combos: frozenset[Combo]) -> tuple[dict[fr... function make_req_file (line 124) | def make_req_file(requirements_directory, regex): FILE: doc/conf.py function exclude_entity (line 250) | def exclude_entity(app, what, name, obj, skip, opts): function setup (line 258) | def setup(app): FILE: econml/_cate_estimator.py class BaseCateEstimator (line 22) | class BaseCateEstimator(metaclass=abc.ABCMeta): method _get_inference_options (line 25) | def _get_inference_options(self): method _get_inference (line 33) | def _get_inference(self, inference): method _set_input_names (line 49) | def _set_input_names(self, Y, T, X, set_flag=False): method _strata (line 61) | def _strata(self, Y, T, *args, **kwargs): method _prefit (line 78) | def _prefit(self, Y, T, *args, **kwargs): method _postfit (line 89) | def _postfit(self, Y, T, *args, **kwargs): method fit (line 94) | def fit(self, *args, inference=None, **kwargs): method _wrap_fit (line 126) | def _wrap_fit(m): method effect (line 144) | def effect(self, X=None, *, T0, T1): method marginal_effect (line 170) | def marginal_effect(self, T, X=None): method ate (line 194) | def ate(self, X=None, *, T0, T1): method cate_feature_names (line 218) | def cate_feature_names(self, feature_names=None): method cate_output_names (line 240) | def cate_output_names(self, output_names=None): method cate_treatment_names (line 263) | def cate_treatment_names(self, treatment_names=None): method marginal_ate (line 286) | def marginal_ate(self, T, X=None): method _expand_treatments (line 310) | def _expand_treatments(self, X=None, *Ts): method _use_inference_method (line 327) | def _use_inference_method(self, name, *args, **kwargs): method _defer_to_inference (line 333) | def _defer_to_inference(m): method effect_interval (line 345) | def effect_interval(self, X=None, *, T0=0, T1=1, alpha=0.05): method effect_inference (line 372) | def effect_inference(self, X=None, *, T0=0, T1=1): method marginal_effect_interval (line 398) | def marginal_effect_interval(self, T, X=None, *, alpha=0.05): method marginal_effect_inference (line 424) | def marginal_effect_inference(self, T, X=None): method ate_interval (line 448) | def ate_interval(self, X=None, *, T0, T1, alpha=0.05): method ate_inference (line 475) | def ate_inference(self, X=None, *, T0, T1): method marginal_ate_interval (line 501) | def marginal_ate_interval(self, T, X=None, *, alpha=0.05): method marginal_ate_inference (line 527) | def marginal_ate_inference(self, T, X=None): method dowhy (line 551) | def dowhy(self): class LinearCateEstimator (line 563) | class LinearCateEstimator(BaseCateEstimator): method const_marginal_effect (line 569) | def const_marginal_effect(self, X=None): method effect (line 592) | def effect(self, X=None, *, T0, T1): method marginal_effect (line 633) | def marginal_effect(self, T, X=None): method marginal_effect_interval (line 680) | def marginal_effect_interval(self, T, X=None, *, alpha=0.05): method marginal_effect_inference (line 691) | def marginal_effect_inference(self, T, X=None): method const_marginal_effect_interval (line 703) | def const_marginal_effect_interval(self, X=None, *, alpha=0.05): method const_marginal_effect_inference (line 727) | def const_marginal_effect_inference(self, X=None): method const_marginal_ate (line 748) | def const_marginal_ate(self, X=None): method const_marginal_ate_interval (line 769) | def const_marginal_ate_interval(self, X=None, *, alpha=0.05): method const_marginal_ate_inference (line 793) | def const_marginal_ate_inference(self, X=None): method marginal_ate (line 813) | def marginal_ate(self, T, X=None): method marginal_ate_interval (line 818) | def marginal_ate_interval(self, T, X=None, *, alpha=0.05): method marginal_ate_inference (line 823) | def marginal_ate_inference(self, T, X=None): method shap_values (line 827) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... class TreatmentExpansionMixin (line 861) | class TreatmentExpansionMixin(BaseCateEstimator): method _prefit (line 871) | def _prefit(self, Y, T, *args, **kwargs): method _postfit (line 877) | def _postfit(self, Y, T, *args, **kwargs): method _expand_treatments (line 882) | def _expand_treatments(self, X=None, *Ts, transform=True): method _set_transformed_treatment_names (line 907) | def _set_transformed_treatment_names(self): method cate_treatment_names (line 917) | def cate_treatment_names(self, treatment_names=None): method effect (line 943) | def effect(self, X=None, *, T0=0, T1=1): method ate (line 948) | def ate(self, X=None, *, T0=0, T1=1): method ate_interval (line 952) | def ate_interval(self, X=None, *, T0=0, T1=1, alpha=0.05): method ate_inference (line 956) | def ate_inference(self, X=None, *, T0=0, T1=1): class LinearModelFinalCateEstimatorMixin (line 961) | class LinearModelFinalCateEstimatorMixin(BaseCateEstimator): method _get_inference_options (line 980) | def _get_inference_options(self): method bias_part_of_coef (line 986) | def bias_part_of_coef(self): method coef_ (line 990) | def coef_(self): method intercept_ (line 1009) | def intercept_(self): method coef__interval (line 1028) | def coef__interval(self, *, alpha=0.05): method coef__inference (line 1046) | def coef__inference(self): method intercept__interval (line 1058) | def intercept__interval(self, *, alpha=0.05): method intercept__inference (line 1076) | def intercept__inference(self): method summary (line 1087) | def summary(self, alpha=0.05, value=0, decimals=3, feature_names=None,... method shap_values (line 1180) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... class StatsModelsCateEstimatorMixin (line 1194) | class StatsModelsCateEstimatorMixin(LinearModelFinalCateEstimatorMixin): method _get_inference_options (line 1205) | def _get_inference_options(self): class DebiasedLassoCateEstimatorMixin (line 1213) | class DebiasedLassoCateEstimatorMixin(LinearModelFinalCateEstimatorMixin): method _get_inference_options (line 1216) | def _get_inference_options(self): class ForestModelFinalCateEstimatorMixin (line 1224) | class ForestModelFinalCateEstimatorMixin(BaseCateEstimator): method _get_inference_options (line 1226) | def _get_inference_options(self): method feature_importances_ (line 1234) | def feature_importances_(self): class LinearModelFinalCateEstimatorDiscreteMixin (line 1238) | class LinearModelFinalCateEstimatorDiscreteMixin(BaseCateEstimator): method _get_inference_options (line 1247) | def _get_inference_options(self): method coef_ (line 1252) | def coef_(self, T): method intercept_ (line 1273) | def intercept_(self, T): method coef__interval (line 1294) | def coef__interval(self, T, *, alpha=0.05): method coef__inference (line 1314) | def coef__inference(self, T): method intercept__interval (line 1331) | def intercept__interval(self, T, *, alpha=0.05): method intercept__inference (line 1351) | def intercept__inference(self, T): method summary (line 1368) | def summary(self, T, *, alpha=0.05, value=0, decimals=3, class StatsModelsCateEstimatorDiscreteMixin (line 1440) | class StatsModelsCateEstimatorDiscreteMixin(LinearModelFinalCateEstimato... method _get_inference_options (line 1450) | def _get_inference_options(self): class DebiasedLassoCateEstimatorDiscreteMixin (line 1458) | class DebiasedLassoCateEstimatorDiscreteMixin(LinearModelFinalCateEstima... method _get_inference_options (line 1461) | def _get_inference_options(self): class ForestModelFinalCateEstimatorDiscreteMixin (line 1469) | class ForestModelFinalCateEstimatorDiscreteMixin(BaseCateEstimator): method _get_inference_options (line 1471) | def _get_inference_options(self): method feature_importances_ (line 1478) | def feature_importances_(self, T): FILE: econml/_ensemble/_ensemble.py function _fit_single_estimator (line 25) | def _fit_single_estimator(estimator, X, y, sample_weight=None, function _set_random_states (line 45) | def _set_random_states(estimator, random_state): class BaseEnsemble (line 79) | class BaseEnsemble(BaseEstimator, metaclass=ABCMeta): method __init__ (line 105) | def __init__(self, base_estimator, *, n_estimators=10, method _validate_estimator (line 116) | def _validate_estimator(self, default=None): method _make_estimator (line 138) | def _make_estimator(self, append=True, random_state=None): method __len__ (line 157) | def __len__(self): method __getitem__ (line 161) | def __getitem__(self, index): method __iter__ (line 165) | def __iter__(self): function _partition_estimators (line 170) | def _partition_estimators(n_estimators, n_jobs): FILE: econml/_ensemble/_utilities.py function _get_n_samples_subsample (line 8) | def _get_n_samples_subsample(n_samples, max_samples): function _accumulate_prediction (line 47) | def _accumulate_prediction(predict, X, out, lock, *args, **kwargs): function _accumulate_prediction_var (line 64) | def _accumulate_prediction_var(predict, X, out, lock, *args, **kwargs): function _accumulate_prediction_and_var (line 91) | def _accumulate_prediction_and_var(predict, X, out, out_var, lock, *args... function _accumulate_oob_preds (line 118) | def _accumulate_oob_preds(tree, X, subsample_inds, alpha_hat, jac_hat, c... FILE: econml/_ortho_learner.py function _fit_fold (line 54) | def _fit_fold(model, train_idxs, test_idxs, calculate_scores, args, kwar... function _crossfit (line 112) | def _crossfit(models: Union[ModelSelector, List[ModelSelector]], folds, ... class _OrthoLearner (line 312) | class _OrthoLearner(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 559) | def __init__(self, *, method _gen_allowed_missing_vars (line 586) | def _gen_allowed_missing_vars(self): method _gen_ortho_learner_model_nuisance (line 590) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 616) | def _gen_ortho_learner_model_final(self): method _check_input_dims (line 638) | def _check_input_dims(self, Y, T, X=None, W=None, Z=None, *other_arrays): method _check_fitted_dims (line 646) | def _check_fitted_dims(self, X): method _check_fitted_dims_w_z (line 652) | def _check_fitted_dims_w_z(self, W, Z): method _subinds_check_none (line 663) | def _subinds_check_none(self, var, inds): method _strata (line 666) | def _strata(self, Y, T, X=None, W=None, Z=None, method _prefit (line 679) | def _prefit(self, Y, T, *args, only_final=False, **kwargs): method fit (line 690) | def fit(self, Y, T, *, X=None, W=None, Z=None, sample_weight=None, fre... method _illegal_refit_inference_methods (line 888) | def _illegal_refit_inference_methods(self): method refit_final (line 891) | def refit_final(self, inference=None): method _fit_nuisances (line 924) | def _fit_nuisances(self, Y, T, X=None, W=None, Z=None, sample_weight=N... method _fit_final (line 977) | def _fit_final(self, Y, T, X=None, W=None, Z=None, nuisances=None, sam... method const_marginal_effect (line 992) | def const_marginal_effect(self, X=None): method const_marginal_effect_interval (line 1006) | def const_marginal_effect_interval(self, X=None, *, alpha=0.05): method const_marginal_effect_inference (line 1017) | def const_marginal_effect_inference(self, X=None): method effect_interval (line 1028) | def effect_interval(self, X=None, *, T0=0, T1=1, alpha=0.05): method effect_inference (line 1040) | def effect_inference(self, X=None, *, T0=0, T1=1): method score (line 1052) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method ortho_learner_model_final_ (line 1161) | def ortho_learner_model_final_(self): method models_nuisance_ (line 1167) | def models_nuisance_(self): FILE: econml/_shap.py function _shap_explain_cme (line 22) | def _shap_explain_cme(cme_model, X, d_t, d_y, function _shap_explain_model_cate (line 90) | def _shap_explain_model_cate(cme_model, models, X, d_t, d_y, featurizer=... function _shap_explain_joint_linear_model_cate (line 187) | def _shap_explain_joint_linear_model_cate(model_final, X, d_t, d_y, fit_... function _shap_explain_multitask_model_cate (line 269) | def _shap_explain_multitask_model_cate(cme_model, multitask_model_cate, ... function _define_names (line 366) | def _define_names(d_t, d_y, treatment_names, output_names, feature_names... FILE: econml/_tree_exporter.py function _color_brew (line 48) | def _color_brew(n): class _TreeExporter (line 90) | class _TreeExporter(_BaseTreeExporter): method node_replacement_text (line 93) | def node_replacement_text(self, tree, node_id, criterion): method node_to_str (line 96) | def node_to_str(self, tree, node_id, criterion): class _MPLExporter (line 110) | class _MPLExporter(_MPLTreeExporter): method __init__ (line 113) | def __init__(self, *args, title=None, **kwargs): method export (line 117) | def export(self, decision_tree, node_dict=None, ax=None): class _DOTExporter (line 127) | class _DOTExporter(_DOTTreeExporter): method __init__ (line 130) | def __init__(self, *args, title=None, **kwargs): method export (line 134) | def export(self, decision_tree, node_dict=None): method tail (line 138) | def tail(self): class _CateTreeMixin (line 145) | class _CateTreeMixin(_TreeExporter): method __init__ (line 148) | def __init__(self, include_uncertainty=False, uncertainty_level=0.1, method get_fill_color (line 155) | def get_fill_color(self, tree, node_id): method node_replacement_text (line 177) | def node_replacement_text(self, tree, node_id, criterion): class _PolicyTreeMixin (line 241) | class _PolicyTreeMixin(_TreeExporter): method __init__ (line 251) | def __init__(self, *args, treatment_names=None, **kwargs): method get_fill_color (line 255) | def get_fill_color(self, tree, node_id): method node_replacement_text (line 267) | def node_replacement_text(self, tree, node_id, criterion): method _node_replacement_text_with_dict (line 287) | def _node_replacement_text_with_dict(self, tree, node_id, criterion): class _PolicyTreeMPLExporter (line 333) | class _PolicyTreeMPLExporter(_PolicyTreeMixin, _MPLExporter): method __init__ (line 368) | def __init__(self, treatment_names=None, title=None, feature_names=None, class _CateTreeMPLExporter (line 380) | class _CateTreeMPLExporter(_CateTreeMixin, _MPLExporter): method __init__ (line 421) | def __init__(self, include_uncertainty, uncertainty_level, title=None, class _PolicyTreeDOTExporter (line 435) | class _PolicyTreeDOTExporter(_PolicyTreeMixin, _DOTExporter): method __init__ (line 481) | def __init__(self, out_file=None, title=None, treatment_names=None, fe... class _CateTreeDOTExporter (line 492) | class _CateTreeDOTExporter(_CateTreeMixin, _DOTExporter): method __init__ (line 544) | def __init__(self, include_uncertainty, uncertainty_level, out_file=No... class _SingleTreeExporterMixin (line 557) | class _SingleTreeExporterMixin(metaclass=abc.ABCMeta): method _make_dot_exporter (line 563) | def _make_dot_exporter(self, *, out_file, feature_names, treatment_nam... method _make_mpl_exporter (line 611) | def _make_mpl_exporter(self, *, title=None, feature_names=None, treatm... method export_graphviz (line 648) | def export_graphviz(self, out_file=None, feature_names=None, treatment... method render (line 718) | def render(self, out_file, format='pdf', view=True, feature_names=None, method plot (line 777) | def plot(self, ax=None, title=None, feature_names=None, treatment_name... FILE: econml/automated_ml/_automated_ml.py function setAutomatedMLWorkspace (line 48) | def setAutomatedMLWorkspace(create_workspace=False, function addAutomatedML (line 114) | def addAutomatedML(baseClass): class AutomatedMLModel (line 141) | class AutomatedMLModel(): method __init__ (line 142) | def __init__(self, automl_config, workspace, experiment_name_prefix="a... method fit (line 170) | def fit(self, X, y, sample_weight=None): method predict (line 204) | def predict(self, X): method predict_proba (line 213) | def predict_proba(self, X): class _InnerAutomatedMLModel (line 223) | class _InnerAutomatedMLModel(): method __init__ (line 225) | def __init__(self, automl_config, workspace, method get_params (line 232) | def get_params(self, deep=True): method fit (line 241) | def fit(self, X, y, sample_weight=None): method predict (line 259) | def predict(self, X): method predict_proba (line 262) | def predict_proba(self, X): class AutomatedMLMixin (line 266) | class AutomatedMLMixin(): method __init__ (line 267) | def __init__(self, *args, **kwargs): method _get_automated_ml_model (line 307) | def _get_automated_ml_model(self, automl_config, prefix): class EconAutoMLConfig (line 320) | class EconAutoMLConfig(AutoMLConfig): method __init__ (line 322) | def __init__(self, sample_weights_required=False, linear_model_require... FILE: econml/cate_interpreter/_interpreters.py class _SingleTreeInterpreter (line 15) | class _SingleTreeInterpreter(_SingleTreeExporterMixin, metaclass=abc.ABC... method interpret (line 18) | def interpret(self, cate_estimator, X): class SingleTreeCateInterpreter (line 35) | class SingleTreeCateInterpreter(_SingleTreeInterpreter): method __init__ (line 136) | def __init__(self, *, method interpret (line 163) | def interpret(self, cate_estimator, X): method _make_dot_exporter (line 212) | def _make_dot_exporter(self, *, out_file, feature_names, treatment_nam... method _make_mpl_exporter (line 223) | def _make_mpl_exporter(self, *, title, feature_names, treatment_names,... class SingleTreePolicyInterpreter (line 235) | class SingleTreePolicyInterpreter(_SingleTreeInterpreter): method __init__ (line 355) | def __init__(self, *, method interpret (line 383) | def interpret(self, cate_estimator, X, sample_treatment_costs=None): method treat (line 479) | def treat(self, X): method _make_dot_exporter (line 498) | def _make_dot_exporter(self, *, out_file, feature_names, treatment_nam... method _make_mpl_exporter (line 512) | def _make_mpl_exporter(self, *, title, feature_names, treatment_names,... FILE: econml/data/dgps.py function ihdp_surface_A (line 11) | def ihdp_surface_A(random_state=None): function ihdp_surface_B (line 45) | def ihdp_surface_B(random_state=None): function _process_ihdp_sim_data (line 80) | def _process_ihdp_sim_data(): FILE: econml/data/dynamic_panel_dgp.py function new_cov_matrix (line 18) | def new_cov_matrix(cov): function linear_approximation (line 45) | def linear_approximation(start, end, e_val): function generate_coefs (line 54) | def generate_coefs(index, columns): function simulate_residuals (line 112) | def simulate_residuals(ind): function simulate_residuals_all (line 137) | def simulate_residuals_all(res_df): function get_prediction (line 148) | def get_prediction(df, coef_matrix, residuals, thetas, n, intervention, ... function generate_dgp (line 168) | def generate_dgp( class AbstracDynamicPanelDGP (line 211) | class AbstracDynamicPanelDGP: method __init__ (line 213) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 219) | def create_instance(self, *args, **kwargs): method _gen_data_with_policy (line 222) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method static_policy_data (line 225) | def static_policy_data(self, n_units, tau, random_seed=123): method adaptive_policy_data (line 230) | def adaptive_policy_data(self, n_units, policy_gen, random_seed=123): method static_policy_effect (line 233) | def static_policy_effect(self, tau, mc_samples=1000): method adaptive_policy_effect (line 241) | def adaptive_policy_effect(self, policy_gen, mc_samples=1000): class DynamicPanelDGP (line 250) | class DynamicPanelDGP(AbstracDynamicPanelDGP): method __init__ (line 252) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 255) | def create_instance(self, s_x, sigma_x, sigma_y, conf_str, epsilon, Al... method hetero_effect_fn (line 320) | def hetero_effect_fn(self, t, x): method _gen_data_with_policy (line 328) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method observational_data (line 351) | def observational_data(self, n_units, gamma, s_t, sigma_t, random_seed... class SemiSynthetic (line 371) | class SemiSynthetic: method create_instance (line 373) | def create_instance(self): method gen_data (line 391) | def gen_data(self, n, n_periods, thetas, random_seed): method plot_coefs (line 445) | def plot_coefs(self): method plot_cov (line 458) | def plot_cov(self): FILE: econml/dml/_rlearner.py class _ModelNuisance (line 40) | class _ModelNuisance(ModelSelector): method __init__ (line 49) | def __init__(self, model_y: ModelSelector, model_t: ModelSelector): method train (line 53) | def train(self, is_selecting, folds, Y, T, X=None, W=None, Z=None, sam... method score (line 61) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method predict (line 70) | def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None, gr... class _ModelFinal (line 81) | class _ModelFinal: method __init__ (line 95) | def __init__(self, model_final): method fit (line 98) | def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, method predict (line 105) | def predict(self, X=None): method score (line 108) | def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_w... method _wrap_scoring (line 139) | def _wrap_scoring(scoring:Union[str, Callable], Y_true, Y_pred, sample... method wrap_scoring (line 179) | def wrap_scoring(scoring, Y_true, Y_pred, sample_weight=None, score_by... class _RLearner (line 196) | class _RLearner(_OrthoLearner): method __init__ (line 378) | def __init__(self, method _gen_model_y (line 405) | def _gen_model_y(self): method _gen_model_t (line 422) | def _gen_model_t(self): method _gen_rlearner_model_final (line 439) | def _gen_rlearner_model_final(self): method _gen_ortho_learner_model_nuisance (line 456) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 459) | def _gen_ortho_learner_model_final(self): method fit (line 462) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method score (line 506) | def score(self, Y, T, X=None, W=None, sample_weight=None, scoring=None): method rlearner_model_final_ (line 540) | def rlearner_model_final_(self): method models_y (line 546) | def models_y(self): method models_t (line 550) | def models_t(self): method nuisance_scores_y (line 554) | def nuisance_scores_y(self): method nuisance_scores_t (line 558) | def nuisance_scores_t(self): method residuals_ (line 562) | def residuals_(self): method scoring_name (line 579) | def scoring_name(scoring: Union[str,Callable,None])->str: method score_nuisances (line 590) | def score_nuisances(self, Y, T, X=None, W=None, Z=None, sample_weight=... FILE: econml/dml/causal_forest.py class _CausalForestFinalWrapper (line 24) | class _CausalForestFinalWrapper: method __init__ (line 26) | def __init__(self, model_final, featurizer, discrete_treatment, drate): method _combine (line 33) | def _combine(self, X, fitting=True): method _ate_and_stderr (line 44) | def _ate_and_stderr(self, drpreds, mask=None): method fit (line 52) | def fit(self, X, T, T_res, Y_res, sample_weight=None, freq_weight=None... method predict (line 95) | def predict(self, X): method ate_ (line 99) | def ate_(self): method ate_ (line 109) | def ate_(self, value): method ate_stderr_ (line 113) | def ate_stderr_(self): method ate_stderr_ (line 123) | def ate_stderr_(self, value): method att_ (line 127) | def att_(self): method att_ (line 137) | def att_(self, value): method att_stderr_ (line 141) | def att_stderr_(self): method att_stderr_ (line 151) | def att_stderr_(self, value): class _GenericSingleOutcomeModelFinalWithCovInference (line 155) | class _GenericSingleOutcomeModelFinalWithCovInference(Inference): method prefit (line 157) | def prefit(self, estimator, *args, **kwargs): method fit (line 161) | def fit(self, estimator, *args, **kwargs): method const_marginal_effect_interval (line 170) | def const_marginal_effect_interval(self, X, *, alpha=0.05): method const_marginal_effect_inference (line 173) | def const_marginal_effect_inference(self, X): method effect_interval (line 184) | def effect_interval(self, X, *, T0, T1, alpha=0.05): method effect_inference (line 187) | def effect_inference(self, X, *, T0, T1): method marginal_effect_interval (line 202) | def marginal_effect_interval(self, T, X, alpha=0.05): method marginal_effect_inference (line 205) | def marginal_effect_inference(self, T, X): class CausalForestDML (line 258) | class CausalForestDML(_BaseDML): method __init__ (line 596) | def __init__(self, *, method _gen_allowed_missing_vars (line 670) | def _gen_allowed_missing_vars(self): method _get_inference_options (line 673) | def _get_inference_options(self): method _gen_featurizer (line 679) | def _gen_featurizer(self): method _gen_model_y (line 682) | def _gen_model_y(self): method _gen_model_t (line 685) | def _gen_model_t(self): method _gen_model_final (line 688) | def _gen_model_final(self): method _gen_rlearner_model_final (line 710) | def _gen_rlearner_model_final(self): method tunable_params (line 715) | def tunable_params(self): method tune (line 721) | def tune(self, Y, T, *, X=None, W=None, method sensitivity_summary (line 822) | def sensitivity_summary(self, null_hypothesis=0, alpha=0.05, c_y=0.05,... method sensitivity_interval (line 851) | def sensitivity_interval(self, alpha=0.05, c_y=0.05, c_t=0.05, rho=1.,... method robustness_value (line 888) | def robustness_value(self, null_hypothesis=0, alpha=0.05, interval_typ... method fit (line 930) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None, method refit_final (line 969) | def refit_final(self, *, inference='auto'): method feature_importances (line 973) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method summary (line 977) | def summary(self, alpha=0.05, value=0, decimals=3, feature_names=None,... method shap_values (line 1057) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... method ate__inference (line 1067) | def ate__inference(self): method ate_ (line 1090) | def ate_(self): method ate_stderr_ (line 1094) | def ate_stderr_(self): method att__inference (line 1097) | def att__inference(self, *, T): method att_ (line 1125) | def att_(self, *, T): method att_stderr_ (line 1144) | def att_stderr_(self, *, T): method feature_importances_ (line 1162) | def feature_importances_(self): method model_final (line 1166) | def model_final(self): method model_final (line 1170) | def model_final(self, model): method __len__ (line 1174) | def __len__(self): method __getitem__ (line 1178) | def __getitem__(self, index): method __iter__ (line 1182) | def __iter__(self): FILE: econml/dml/dml.py function _combine (line 33) | def _combine(X, W, n_samples): class _FirstStageWrapper (line 40) | class _FirstStageWrapper: method __init__ (line 41) | def __init__(self, model, discrete_target): method predict (line 45) | def predict(self, X, W): method score (line 58) | def score(self, X, W, Target, sample_weight=None, scoring=None, score_... method _wrap_scoring (line 90) | def _wrap_scoring(scoring, Y_true, X, est, sample_weight=None, score_b... class _FirstStageSelector (line 95) | class _FirstStageSelector(SingleModelSelector): method __init__ (line 96) | def __init__(self, model: SingleModelSelector, discrete_target): method train (line 100) | def train(self, is_selecting, folds, X, W, Target, sample_weight=None,... method best_model (line 115) | def best_model(self): method best_score (line 119) | def best_score(self): function _make_first_stage_selector (line 123) | def _make_first_stage_selector(model, is_discrete, random_state): class _FinalWrapper (line 132) | class _FinalWrapper: method __init__ (line 133) | def __init__(self, model_final, fit_cate_intercept, featurizer, method _combine (line 156) | def _combine(self, X, T, fitting=True): method fit (line 172) | def fit(self, X, T, T_res, Y_res, sample_weight=None, freq_weight=None... method predict (line 221) | def predict(self, X): class _BaseDML (line 233) | class _BaseDML(_RLearner): method original_featurizer (line 238) | def original_featurizer(self): method featurizer_ (line 244) | def featurizer_(self): method model_final_ (line 250) | def model_final_(self): method model_cate (line 256) | def model_cate(self): method models_y (line 269) | def models_y(self): method models_t (line 283) | def models_t(self): method cate_feature_names (line 296) | def cate_feature_names(self, feature_names=None): class DML (line 324) | class DML(LinearModelFinalCateEstimatorMixin, _BaseDML): method __init__ (line 518) | def __init__(self, *, method _gen_allowed_missing_vars (line 557) | def _gen_allowed_missing_vars(self): method _gen_featurizer (line 560) | def _gen_featurizer(self): method _gen_model_y (line 563) | def _gen_model_y(self): method _gen_model_t (line 566) | def _gen_model_t(self): method _gen_model_final (line 569) | def _gen_model_final(self): method _gen_rlearner_model_final (line 572) | def _gen_rlearner_model_final(self): method fit (line 577) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method refit_final (line 621) | def refit_final(self, *, inference='auto'): method bias_part_of_coef (line 626) | def bias_part_of_coef(self): method fit_cate_intercept_ (line 630) | def fit_cate_intercept_(self): method sensitivity_summary (line 633) | def sensitivity_summary(self, null_hypothesis=0, alpha=0.05, c_y=0.05,... method sensitivity_interval (line 663) | def sensitivity_interval(self, alpha=0.05, c_y=0.05, c_t=0.05, rho=1.,... method robustness_value (line 700) | def robustness_value(self, null_hypothesis=0, alpha=0.05, interval_typ... class LinearDML (line 742) | class LinearDML(StatsModelsCateEstimatorMixin, DML): method __init__ (line 872) | def __init__(self, *, method _gen_allowed_missing_vars (line 910) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 913) | def _gen_model_final(self): method fit (line 917) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method model_final (line 962) | def model_final(self): method model_final (line 966) | def model_final(self, model): class SparseLinearDML (line 971) | class SparseLinearDML(DebiasedLassoCateEstimatorMixin, DML): method __init__ (line 1133) | def __init__(self, *, method _gen_allowed_missing_vars (line 1182) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 1185) | def _gen_model_final(self): method fit (line 1196) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None, method model_final (line 1238) | def model_final(self): method model_final (line 1242) | def model_final(self, model): class _RandomFeatures (line 1247) | class _RandomFeatures(TransformerMixin): method __init__ (line 1248) | def __init__(self, *, dim, bw, random_state): method fit (line 1253) | def fit(self, X): method transform (line 1260) | def transform(self, X): class KernelDML (line 1264) | class KernelDML(DML): method __init__ (line 1379) | def __init__(self, model_y='auto', model_t='auto', method _gen_allowed_missing_vars (line 1413) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 1416) | def _gen_model_final(self): method _gen_featurizer (line 1419) | def _gen_featurizer(self): method fit (line 1422) | def fit(self, Y, T, X=None, W=None, *, sample_weight=None, groups=None, method featurizer (line 1459) | def featurizer(self): method featurizer (line 1463) | def featurizer(self, value): method model_final (line 1468) | def model_final(self): method model_final (line 1472) | def model_final(self, model): class NonParamDML (line 1477) | class NonParamDML(_BaseDML): method __init__ (line 1603) | def __init__(self, *, method _gen_allowed_missing_vars (line 1636) | def _gen_allowed_missing_vars(self): method _get_inference_options (line 1639) | def _get_inference_options(self): method _gen_featurizer (line 1645) | def _gen_featurizer(self): method _gen_model_y (line 1648) | def _gen_model_y(self): method _gen_model_t (line 1652) | def _gen_model_t(self): method _gen_model_final (line 1656) | def _gen_model_final(self): method _gen_rlearner_model_final (line 1659) | def _gen_rlearner_model_final(self): method fit (line 1664) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method refit_final (line 1708) | def refit_final(self, *, inference='auto'): method shap_values (line 1712) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... FILE: econml/dowhy.py class DoWhyWrapper (line 27) | class DoWhyWrapper: method __init__ (line 37) | def __init__(self, cate_estimator): method _get_params (line 43) | def _get_params(self): method fit (line 62) | def fit(self, Y, T, X=None, W=None, Z=None, *, outcome_names=None, tre... method refute_estimate (line 191) | def refute_estimate(self, *, method_name, **kwargs): method refit_final (line 226) | def refit_final(self, inference=None): method __getattr__ (line 230) | def __getattr__(self, attr): method __setattr__ (line 255) | def __setattr__(self, attr, value): FILE: econml/dr/_drlearner.py function _calculate_crump_threshold (line 59) | def _calculate_crump_threshold(propensities): class _ModelNuisance (line 109) | class _ModelNuisance(ModelSelector): method __init__ (line 110) | def __init__(self, method _combine (line 120) | def _combine(self, X, W): method train (line 123) | def train(self, is_selecting, folds, Y, T, X=None, W=None, *, sample_w... method score (line 139) | def score(self, Y, T, X=None, W=None, *, sample_weight=None, groups=No... method predict (line 148) | def predict(self, Y, T, X=None, W=None, *, sample_weight=None, groups=... function _make_first_stage_selector (line 192) | def _make_first_stage_selector(model, is_discrete, random_state): class _ModelFinal (line 198) | class _ModelFinal: method __init__ (line 204) | def __init__(self, model_final, featurizer, multitask_model_final, tri... method _compute_trim_mask (line 211) | def _compute_trim_mask(self, T_pred): method fit (line 246) | def fit(self, Y, T, X=None, W=None, *, nuisances, method predict (line 300) | def predict(self, X=None): method score (line 312) | def score(self, Y, T, X=None, W=None, *, nuisances, sample_weight=None... class DRLearner (line 344) | class DRLearner(_OrthoLearner): method __init__ (line 587) | def __init__(self, *, method _gen_allowed_missing_vars (line 626) | def _gen_allowed_missing_vars(self): method const_marginal_effect (line 630) | def const_marginal_effect(self, X=None): method const_marginal_ate (line 653) | def const_marginal_ate(self, X=None): method _get_inference_options (line 672) | def _get_inference_options(self): method _gen_ortho_learner_model_nuisance (line 680) | def _gen_ortho_learner_model_nuisance(self): method _gen_featurizer (line 686) | def _gen_featurizer(self): method _gen_model_final (line 689) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 692) | def _gen_ortho_learner_model_final(self): method fit (line 696) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method refit_final (line 751) | def refit_final(self, *, inference='auto'): method score (line 755) | def score(self, Y, T, X=None, W=None, sample_weight=None): method multitask_model_cate (line 788) | def multitask_model_cate(self): method model_cate (line 803) | def model_cate(self, T=1): method models_propensity (line 826) | def models_propensity(self): method models_regression (line 840) | def models_regression(self): method nuisance_scores_propensity (line 854) | def nuisance_scores_propensity(self): method nuisance_scores_regression (line 859) | def nuisance_scores_regression(self): method n_samples_trimmed_ (line 864) | def n_samples_trimmed_(self): method n_samples_used_ (line 880) | def n_samples_used_(self): method featurizer_ (line 896) | def featurizer_(self): method cate_feature_names (line 908) | def cate_feature_names(self, feature_names=None): method model_final_ (line 936) | def model_final_(self): method fitted_models_final (line 940) | def fitted_models_final(self): method shap_values (line 943) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... method sensitivity_summary (line 964) | def sensitivity_summary(self, T, null_hypothesis=0, alpha=0.05, c_y=0.... method sensitivity_interval (line 997) | def sensitivity_interval(self, T, alpha=0.05, c_y=0.05, c_t=0.05, rho=... method robustness_value (line 1040) | def robustness_value(self, T, null_hypothesis=0, alpha=0.05, interval_... class LinearDRLearner (line 1086) | class LinearDRLearner(StatsModelsCateEstimatorDiscreteMixin, DRLearner): method __init__ (line 1276) | def __init__(self, *, method _gen_allowed_missing_vars (line 1314) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 1317) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 1321) | def _gen_ortho_learner_model_final(self): method fit (line 1324) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, freq_weight... method fit_cate_intercept_ (line 1369) | def fit_cate_intercept_(self): method multitask_model_cate (line 1373) | def multitask_model_cate(self): method multitask_model_final (line 1379) | def multitask_model_final(self): method multitask_model_final (line 1383) | def multitask_model_final(self, value): method model_final (line 1388) | def model_final(self): method model_final (line 1392) | def model_final(self, model): class SparseLinearDRLearner (line 1397) | class SparseLinearDRLearner(DebiasedLassoCateEstimatorDiscreteMixin, DRL... method __init__ (line 1611) | def __init__(self, *, method _gen_allowed_missing_vars (line 1660) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 1663) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 1674) | def _gen_ortho_learner_model_final(self): method fit (line 1677) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None, method fit_cate_intercept_ (line 1720) | def fit_cate_intercept_(self): method multitask_model_final (line 1724) | def multitask_model_final(self): method multitask_model_final (line 1728) | def multitask_model_final(self, value): method model_final (line 1733) | def model_final(self): method model_final (line 1737) | def model_final(self, model): class ForestDRLearner (line 1742) | class ForestDRLearner(ForestModelFinalCateEstimatorDiscreteMixin, DRLear... method __init__ (line 1952) | def __init__(self, *, method _gen_allowed_missing_vars (line 2010) | def _gen_allowed_missing_vars(self): method _gen_model_final (line 2013) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 2031) | def _gen_ortho_learner_model_final(self): method fit (line 2034) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None, method multitask_model_cate (line 2073) | def multitask_model_cate(self): method multitask_model_final (line 2078) | def multitask_model_final(self): method multitask_model_final (line 2082) | def multitask_model_final(self, value): method model_final (line 2087) | def model_final(self): method model_final (line 2091) | def model_final(self, model): FILE: econml/federated_learning.py class FederatedEstimator (line 19) | class FederatedEstimator(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 30) | def __init__(self, estimators: List[LinearDML]): method _gen_allowed_missing_vars (line 75) | def _gen_allowed_missing_vars(self): method const_marginal_effect (line 81) | def const_marginal_effect(self, X=None): method fit (line 85) | def fit(self, *args, **kwargs): method bias_part_of_coef (line 90) | def bias_part_of_coef(self): FILE: econml/grf/_base_grf.py class BaseGRF (line 40) | class BaseGRF(BaseEnsemble, metaclass=ABCMeta): method __init__ (line 56) | def __init__(self, method _get_alpha_and_pointJ (line 108) | def _get_alpha_and_pointJ(self, X, T, y, **kwargs): method _get_n_outputs_decomposition (line 127) | def _get_n_outputs_decomposition(self, X, T, y, **kwargs): method apply (line 145) | def apply(self, X): method decision_path (line 168) | def decision_path(self, X): method fit (line 199) | def fit(self, X, T, y, *, sample_weight=None, **kwargs): method get_subsample_inds (line 409) | def get_subsample_inds(self,): method feature_importances (line 424) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 465) | def feature_importances_(self): method _validate_X_predict (line 468) | def _validate_X_predict(self, X): method predict_tree_average_full (line 474) | def predict_tree_average_full(self, X): method predict_tree_average (line 513) | def predict_tree_average(self, X): method predict_moment_and_var (line 537) | def predict_moment_and_var(self, X, parameter, slice=None, parallel=Tr... method predict_alpha_and_jac (line 607) | def predict_alpha_and_jac(self, X, slice=None, parallel=True): method _predict_point_and_var (line 665) | def _predict_point_and_var(self, X, full=False, point=True, var=False,... method predict_full (line 791) | def predict_full(self, X, interval=False, alpha=0.05): method predict (line 827) | def predict(self, X, interval=False, alpha=0.05): method predict_interval (line 862) | def predict_interval(self, X, alpha=0.05): method predict_and_var (line 884) | def predict_and_var(self, X): method predict_var (line 903) | def predict_var(self, X): method prediction_stderr (line 920) | def prediction_stderr(self, X): method _check_projector (line 937) | def _check_projector(self, X, projector): method predict_projection_and_var (line 947) | def predict_projection_and_var(self, X, projector): method predict_projection (line 976) | def predict_projection(self, X, projector): method predict_projection_var (line 1001) | def predict_projection_var(self, X, projector): method oob_predict (line 1026) | def oob_predict(self, Xtrain): FILE: econml/grf/_base_grftree.py class GRFTree (line 31) | class GRFTree(BaseTree): method __init__ (line 288) | def __init__(self, *, method _get_valid_criteria (line 316) | def _get_valid_criteria(self): method _get_valid_min_var_leaf_criteria (line 319) | def _get_valid_min_var_leaf_criteria(self): method _get_store_jac (line 322) | def _get_store_jac(self): method init (line 325) | def init(self,): method fit (line 340) | def fit(self, X, y, n_y, n_outputs, n_relevant_outputs, sample_weight=... method predict (line 372) | def predict(self, X, check_input=True): method predict_full (line 394) | def predict_full(self, X, check_input=True): method predict_alpha_and_jac (line 416) | def predict_alpha_and_jac(self, X, check_input=True): method predict_moment (line 440) | def predict_moment(self, X, parameter, check_input=True): method feature_importances (line 467) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 498) | def feature_importances_(self): FILE: econml/grf/classes.py class MultiOutputGRF (line 21) | class MultiOutputGRF(BaseEstimator): method __init__ (line 28) | def __init__(self, estimator): method fit (line 31) | def fit(self, X, T, y, *, sample_weight=None, **kwargs): method predict (line 41) | def predict(self, X, interval=False, alpha=0.05): method predict_and_var (line 50) | def predict_and_var(self, X): method predict_projection_and_var (line 54) | def predict_projection_and_var(self, X, projector): method oob_predict (line 58) | def oob_predict(self, Xtrain): method feature_importances (line 62) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 68) | def feature_importances_(self): method __len__ (line 71) | def __len__(self): method __getitem__ (line 75) | def __getitem__(self, index): method __iter__ (line 79) | def __iter__(self): class CausalForest (line 88) | class CausalForest(BaseGRF): method __init__ (line 342) | def __init__(self, method fit (line 373) | def fit(self, X, T, y, *, sample_weight=None): method _get_alpha_and_pointJ (line 397) | def _get_alpha_and_pointJ(self, X, T, y): method _get_n_outputs_decomposition (line 404) | def _get_n_outputs_decomposition(self, X, T, y): class CausalIVForest (line 412) | class CausalIVForest(BaseGRF): method __init__ (line 675) | def __init__(self, method fit (line 706) | def fit(self, X, T, y, *, Z, sample_weight=None): method _get_alpha_and_pointJ (line 735) | def _get_alpha_and_pointJ(self, X, T, y, *, Z): method _get_n_outputs_decomposition (line 754) | def _get_n_outputs_decomposition(self, X, T, y, *, Z): class RegressionForest (line 762) | class RegressionForest(BaseGRF): method __init__ (line 978) | def __init__(self, method fit (line 1005) | def fit(self, X, y, *, sample_weight=None): method _get_alpha_and_pointJ (line 1027) | def _get_alpha_and_pointJ(self, X, y, T): method _get_n_outputs_decomposition (line 1031) | def _get_n_outputs_decomposition(self, X, y, T): FILE: econml/inference/_bootstrap.py class BootstrapEstimator (line 11) | class BootstrapEstimator: method __init__ (line 54) | def __init__(self, wrapped, method __stratified_indices (line 71) | def __stratified_indices(arr): method fit (line 80) | def fit(self, *args, **named_args): method __getattr__ (line 127) | def __getattr__(self, name): FILE: econml/inference/_inference.py class Inference (line 22) | class Inference(metaclass=abc.ABCMeta): method prefit (line 23) | def prefit(self, estimator, *args, **kwargs): method fit (line 28) | def fit(self, estimator, *args, **kwargs): method ate_interval (line 36) | def ate_interval(self, X=None, *, T0=0, T1=1, alpha=0.05): method ate_inference (line 39) | def ate_inference(self, X=None, *, T0=0, T1=1): method marginal_ate_interval (line 42) | def marginal_ate_interval(self, T, X=None, *, alpha=0.05): method marginal_ate_inference (line 45) | def marginal_ate_inference(self, T, X=None): method const_marginal_ate_interval (line 48) | def const_marginal_ate_interval(self, X=None, *, alpha=0.05): method const_marginal_ate_inference (line 51) | def const_marginal_ate_inference(self, X=None): class BootstrapInference (line 55) | class BootstrapInference(Inference): method __init__ (line 80) | def __init__(self, n_bootstrap_samples=100, n_jobs=-1, bootstrap_type=... method fit (line 86) | def fit(self, estimator, *args, **kwargs): method __getattr__ (line 97) | def __getattr__(self, name): class GenericModelFinalInference (line 110) | class GenericModelFinalInference(Inference): method prefit (line 120) | def prefit(self, estimator, *args, **kwargs): method fit (line 124) | def fit(self, estimator, *args, **kwargs): method const_marginal_effect_interval (line 133) | def const_marginal_effect_interval(self, X, *, alpha=0.05): method const_marginal_effect_inference (line 136) | def const_marginal_effect_inference(self, X): method _predict (line 156) | def _predict(self, X): method _prediction_stderr (line 159) | def _prediction_stderr(self, X): class GenericSingleTreatmentModelFinalInference (line 167) | class GenericSingleTreatmentModelFinalInference(GenericModelFinalInferen... method fit (line 176) | def fit(self, estimator, *args, **kwargs): method effect_interval (line 182) | def effect_interval(self, X, *, T0, T1, alpha=0.05): method effect_inference (line 185) | def effect_inference(self, X, *, T0, T1): method marginal_effect_inference (line 206) | def marginal_effect_inference(self, T, X): method marginal_effect_interval (line 238) | def marginal_effect_interval(self, T, X, *, alpha=0.05): class LinearModelFinalInference (line 242) | class LinearModelFinalInference(GenericModelFinalInference): method fit (line 252) | def fit(self, estimator, *args, **kwargs): method _predict (line 261) | def _predict(self, X): method effect_interval (line 273) | def effect_interval(self, X, *, T0, T1, alpha=0.05): method effect_inference (line 276) | def effect_inference(self, X, *, T0, T1): method const_marginal_effect_inference (line 299) | def const_marginal_effect_inference(self, X): method marginal_effect_inference (line 316) | def marginal_effect_inference(self, T, X): method marginal_effect_interval (line 374) | def marginal_effect_interval(self, T, X, *, alpha=0.05): method coef__interval (line 377) | def coef__interval(self, *, alpha=0.05): method coef__inference (line 388) | def coef__inference(self): method intercept__interval (line 419) | def intercept__interval(self, *, alpha=0.05): method intercept__inference (line 432) | def intercept__inference(self): class StatsModelsInference (line 459) | class StatsModelsInference(LinearModelFinalInference): method __init__ (line 471) | def __init__(self, cov_type='HC1'): method prefit (line 479) | def prefit(self, estimator, *args, **kwargs): class GenericModelFinalInferenceDiscrete (line 486) | class GenericModelFinalInferenceDiscrete(Inference): method prefit (line 493) | def prefit(self, estimator, *args, **kwargs): method fit (line 497) | def fit(self, estimator, *args, **kwargs): method const_marginal_effect_interval (line 509) | def const_marginal_effect_interval(self, X, *, alpha=0.05): method const_marginal_effect_inference (line 512) | def const_marginal_effect_inference(self, X): method effect_interval (line 533) | def effect_interval(self, X, *, T0, T1, alpha=0.05): method effect_inference (line 536) | def effect_inference(self, X, *, T0, T1): class LinearModelFinalInferenceDiscrete (line 561) | class LinearModelFinalInferenceDiscrete(GenericModelFinalInferenceDiscre... method const_marginal_effect_inference (line 569) | def const_marginal_effect_inference(self, X): method effect_inference (line 584) | def effect_inference(self, X, *, T0, T1): method coef__interval (line 596) | def coef__interval(self, T, *, alpha=0.05): method coef__inference (line 602) | def coef__inference(self, T): method intercept__interval (line 627) | def intercept__interval(self, T, *, alpha=0.05): method intercept__inference (line 635) | def intercept__inference(self, T): class StatsModelsInferenceDiscrete (line 655) | class StatsModelsInferenceDiscrete(LinearModelFinalInferenceDiscrete): method __init__ (line 666) | def __init__(self, cov_type='HC1'): method prefit (line 674) | def prefit(self, estimator, *args, **kwargs): class InferenceResults (line 680) | class InferenceResults(metaclass=abc.ABCMeta): method __init__ (line 702) | def __init__(self, d_t, d_y, pred, inf_type, fname_transformer=None, method point_estimate (line 716) | def point_estimate(self): method stderr (line 732) | def stderr(self): method var (line 747) | def var(self): method conf_int (line 764) | def conf_int(self, alpha=0.05): method pvalue (line 785) | def pvalue(self, value=0): method zstat (line 804) | def zstat(self, value=0): method summary_frame (line 825) | def summary_frame(self, alpha=0.05, value=0, decimals=3, method population_summary (line 908) | def population_summary(self, alpha=0.05, value=0, decimals=3, tol=0.00... method _reshape_array (line 945) | def _reshape_array(self, arr): method _expand_outputs (line 954) | def _expand_outputs(self, n_rows): method translate (line 973) | def translate(self, offset): method scale (line 987) | def scale(self, factor): class NormalInferenceResults (line 1001) | class NormalInferenceResults(InferenceResults): method __init__ (line 1032) | def __init__(self, d_t, d_y, pred, pred_stderr, mean_pred_stderr, inf_... method stderr (line 1040) | def stderr(self): method conf_int (line 1054) | def conf_int(self, alpha=0.05): method pvalue (line 1078) | def pvalue(self, value=0): method population_summary (line 1097) | def population_summary(self, alpha=0.05, value=0, decimals=3, tol=0.00... method _expand_outputs (line 1104) | def _expand_outputs(self, n_rows): method scale (line 1114) | def scale(self, factor): class EmpiricalInferenceResults (line 1127) | class EmpiricalInferenceResults(InferenceResults): method __init__ (line 1150) | def __init__(self, d_t, d_y, pred, pred_dist, inf_type, fname_transfor... method stderr (line 1156) | def stderr(self): method conf_int (line 1170) | def conf_int(self, alpha=0.05): method pvalue (line 1192) | def pvalue(self, value=0): method _expand_outputs (line 1214) | def _expand_outputs(self, n_rows): method translate (line 1221) | def translate(self, other): method scale (line 1230) | def scale(self, factor): class PopulationSummaryResults (line 1240) | class PopulationSummaryResults: method __init__ (line 1280) | def __init__(self, pred, pred_stderr, mean_pred_stderr, d_t, d_y, alph... method __str__ (line 1296) | def __str__(self): method _repr_html_ (line 1299) | def _repr_html_(self): method mean_point (line 1304) | def mean_point(self): method stderr_mean (line 1319) | def stderr_mean(self): method zstat (line 1339) | def zstat(self, *, value=None): method pvalue (line 1360) | def pvalue(self, *, value=None): method conf_int_mean (line 1381) | def conf_int_mean(self, *, alpha=None): method std_point (line 1406) | def std_point(self): method percentile_point (line 1420) | def percentile_point(self, *, alpha=None): method conf_int_point (line 1443) | def conf_int_point(self, *, alpha=None, tol=None): method stderr_point (line 1472) | def stderr_point(self): method summary (line 1486) | def summary(self, alpha=None, value=None, decimals=None, tol=None, out... method _print (line 1515) | def _print(self, *, alpha=None, value=None, decimals=None, tol=None, o... method _mixture_ppf (line 1577) | def _mixture_ppf(self, alpha, mean, stderr, tol): method _format_res (line 1604) | def _format_res(self, res, decimals): method _get_stub_names (line 1609) | def _get_stub_names(self, d_y, d_t, treatment_names, output_names): FILE: econml/iv/dml/_dml.py function _combine (line 34) | def _combine(W, Z, n_samples): class _OrthoIVNuisanceSelector (line 41) | class _OrthoIVNuisanceSelector(ModelSelector): method __init__ (line 43) | def __init__(self, method train (line 56) | def train(self, is_selecting, folds, Y, T, X=None, W=None, Z=None, sam... method score (line 68) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method predict (line 93) | def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None, gr... class _OrthoIVModelFinal (line 120) | class _OrthoIVModelFinal: method __init__ (line 121) | def __init__(self, model_final, featurizer, fit_cate_intercept): method _combine (line 137) | def _combine(self, X, T, fitting=True): method fit (line 149) | def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, method predict (line 166) | def predict(self, X=None): method score (line 174) | def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_w... class OrthoIV (line 189) | class OrthoIV(LinearModelFinalCateEstimatorMixin, _OrthoLearner): method __init__ (line 362) | def __init__(self, *, method _gen_allowed_missing_vars (line 399) | def _gen_allowed_missing_vars(self): method _gen_featurizer (line 402) | def _gen_featurizer(self): method _gen_model_final (line 405) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 408) | def _gen_ortho_learner_model_final(self): method _gen_ortho_learner_model_nuisance (line 411) | def _gen_ortho_learner_model_nuisance(self): method fit (line 436) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method refit_final (line 488) | def refit_final(self, *, inference='auto'): method score (line 492) | def score(self, Y, T, Z, X=None, W=None, sample_weight=None): method featurizer_ (line 528) | def featurizer_(self): method original_featurizer (line 541) | def original_featurizer(self): method cate_feature_names (line 546) | def cate_feature_names(self, feature_names=None): method model_final_ (line 574) | def model_final_(self): method model_cate (line 579) | def model_cate(self): method models_y_xw (line 592) | def models_y_xw(self): method models_t_xw (line 606) | def models_t_xw(self): method models_z_xw (line 620) | def models_z_xw(self): method models_t_xwz (line 636) | def models_t_xwz(self): method nuisance_scores_y_xw (line 652) | def nuisance_scores_y_xw(self): method nuisance_scores_t_xw (line 657) | def nuisance_scores_t_xw(self): method nuisance_scores_z_xw (line 662) | def nuisance_scores_z_xw(self): method nuisance_scores_t_xwz (line 669) | def nuisance_scores_t_xwz(self): method fit_cate_intercept_ (line 676) | def fit_cate_intercept_(self): method bias_part_of_coef (line 680) | def bias_part_of_coef(self): method model_final (line 684) | def model_final(self): method model_final (line 688) | def model_final(self, model): method residuals_ (line 693) | def residuals_(self): class _BaseDMLIVNuisanceSelector (line 710) | class _BaseDMLIVNuisanceSelector(ModelSelector): method __init__ (line 718) | def __init__(self, model_y_xw: ModelSelector, model_t_xw: ModelSelecto... method train (line 723) | def train(self, is_selecting, folds, Y, T, X=None, W=None, Z=None, sam... method score (line 734) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method predict (line 752) | def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None, gr... class _BaseDMLIVModelFinal (line 767) | class _BaseDMLIVModelFinal(_ModelFinal): class _BaseDMLIV (line 784) | class _BaseDMLIV(_OrthoLearner): method fit (line 788) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method score (line 832) | def score(self, Y, T, Z, X=None, W=None, sample_weight=None): method original_featurizer (line 867) | def original_featurizer(self): method featurizer_ (line 871) | def featurizer_(self): method model_final_ (line 877) | def model_final_(self): method model_cate (line 882) | def model_cate(self): method models_y_xw (line 895) | def models_y_xw(self): method models_t_xw (line 909) | def models_t_xw(self): method models_t_xwz (line 923) | def models_t_xwz(self): method nuisance_scores_y_xw (line 937) | def nuisance_scores_y_xw(self): method nuisance_scores_t_xw (line 942) | def nuisance_scores_t_xw(self): method nuisance_scores_t_xwz (line 947) | def nuisance_scores_t_xwz(self): method residuals_ (line 952) | def residuals_(self): method cate_feature_names (line 968) | def cate_feature_names(self, feature_names=None): class DMLIV (line 996) | class DMLIV(_BaseDMLIV): method __init__ (line 1154) | def __init__(self, *, method _gen_featurizer (line 1188) | def _gen_featurizer(self): method _gen_model_y_xw (line 1191) | def _gen_model_y_xw(self): method _gen_model_t_xw (line 1194) | def _gen_model_t_xw(self): method _gen_model_t_xwz (line 1197) | def _gen_model_t_xwz(self): method _gen_model_final (line 1200) | def _gen_model_final(self): method _gen_ortho_learner_model_nuisance (line 1203) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 1206) | def _gen_ortho_learner_model_final(self): method bias_part_of_coef (line 1213) | def bias_part_of_coef(self): method fit_cate_intercept_ (line 1217) | def fit_cate_intercept_(self): method shap_values (line 1220) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... method coef_ (line 1234) | def coef_(self): method intercept_ (line 1253) | def intercept_(self): method summary (line 1271) | def summary(self, decimals=3, feature_names=None, treatment_names=None... class NonParamDMLIV (line 1378) | class NonParamDMLIV(_BaseDMLIV): method __init__ (line 1543) | def __init__(self, *, method _gen_featurizer (line 1575) | def _gen_featurizer(self): method _gen_model_y_xw (line 1578) | def _gen_model_y_xw(self): method _gen_model_t_xw (line 1581) | def _gen_model_t_xw(self): method _gen_model_t_xwz (line 1584) | def _gen_model_t_xwz(self): method _gen_model_final (line 1587) | def _gen_model_final(self): method _gen_ortho_learner_model_nuisance (line 1590) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 1593) | def _gen_ortho_learner_model_final(self): method shap_values (line 1599) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... FILE: econml/iv/dr/_dr.py function _combine (line 37) | def _combine(W, Z, n_samples): class _BaseDRIVNuisanceSelector (line 44) | class _BaseDRIVNuisanceSelector(ModelSelector): method __init__ (line 45) | def __init__(self, *, prel_model_effect, model_y_xw, model_t_xw, model_z, method train (line 59) | def train(self, is_selecting, folds, Y, T, X=None, W=None, Z=None, sam... method score (line 83) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method predict (line 124) | def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None, gr... class _BaseDRIVNuisanceCovarianceSelector (line 165) | class _BaseDRIVNuisanceCovarianceSelector(ModelSelector): method __init__ (line 166) | def __init__(self, *, model_tz_xw, method _get_target (line 175) | def _get_target(self, T_res, Z_res, T, Z): method train (line 216) | def train(self, is_selecting, folds, method score (line 226) | def score(self, prel_theta, Y_res, T_res, Z_res, Y, T, X=None, W=None,... method predict (line 236) | def predict(self, prel_theta, Y_res, T_res, Z_res, Y, T, X=None, W=Non... class _BaseDRIVModelFinal (line 271) | class _BaseDRIVModelFinal: method __init__ (line 272) | def __init__(self, model_final, featurizer, fit_cate_intercept, cov_cl... method _effect_estimate (line 290) | def _effect_estimate(self, nuisances): method _transform_X (line 305) | def _transform_X(self, X, n=1, fitting=True): method fit (line 317) | def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, method predict (line 333) | def predict(self, X=None): method score (line 337) | def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_w... class _BaseDRIV (line 351) | class _BaseDRIV(_OrthoLearner): method __init__ (line 354) | def __init__(self, *, method _gen_allowed_missing_vars (line 390) | def _gen_allowed_missing_vars(self): method _get_inference_options (line 394) | def _get_inference_options(self): method _gen_featurizer (line 399) | def _gen_featurizer(self): method _gen_model_final (line 402) | def _gen_model_final(self): method _gen_ortho_learner_model_final (line 405) | def _gen_ortho_learner_model_final(self): method _check_inputs (line 409) | def _check_inputs(self, Y, T, Z, X, W): method fit (line 427) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method refit_final (line 474) | def refit_final(self, *, inference='auto'): method score (line 478) | def score(self, Y, T, Z, X=None, W=None, sample_weight=None): method featurizer_ (line 514) | def featurizer_(self): method original_featurizer (line 527) | def original_featurizer(self): method cate_feature_names (line 532) | def cate_feature_names(self, feature_names=None): method model_final_ (line 560) | def model_final_(self): method model_cate (line 565) | def model_cate(self): method shap_values (line 577) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... method residuals_ (line 588) | def residuals_(self): class _DRIV (line 606) | class _DRIV(_BaseDRIV): method __init__ (line 609) | def __init__(self, *, method _gen_prel_model_effect (line 662) | def _gen_prel_model_effect(self): method _gen_ortho_learner_model_nuisance (line 665) | def _gen_ortho_learner_model_nuisance(self): class DRIV (line 705) | class DRIV(_DRIV): method __init__ (line 907) | def __init__(self, *, method _gen_model_final (line 970) | def _gen_model_final(self): method _gen_prel_model_effect (line 975) | def _gen_prel_model_effect(self): method fit (line 1019) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method models_y_xw (line 1072) | def models_y_xw(self): method models_t_xw (line 1086) | def models_t_xw(self): method models_z_xw (line 1100) | def models_z_xw(self): method models_t_xwz (line 1116) | def models_t_xwz(self): method models_tz_xw (line 1132) | def models_tz_xw(self): method models_prel_model_effect (line 1146) | def models_prel_model_effect(self): method nuisance_scores_y_xw (line 1160) | def nuisance_scores_y_xw(self): method nuisance_scores_t_xw (line 1165) | def nuisance_scores_t_xw(self): method nuisance_scores_z_xw (line 1170) | def nuisance_scores_z_xw(self): method nuisance_scores_t_xwz (line 1177) | def nuisance_scores_t_xwz(self): method nuisance_scores_prel_model_effect (line 1184) | def nuisance_scores_prel_model_effect(self): method nuisance_scores_tz_xw (line 1189) | def nuisance_scores_tz_xw(self): class LinearDRIV (line 1194) | class LinearDRIV(StatsModelsCateEstimatorMixin, DRIV): method __init__ (line 1408) | def __init__(self, *, method _gen_model_final (line 1466) | def _gen_model_final(self): method fit (line 1469) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method fit_cate_intercept_ (line 1516) | def fit_cate_intercept_(self): method bias_part_of_coef (line 1520) | def bias_part_of_coef(self): method model_final (line 1524) | def model_final(self): method model_final (line 1528) | def model_final(self, model): class SparseLinearDRIV (line 1533) | class SparseLinearDRIV(DebiasedLassoCateEstimatorMixin, DRIV): method __init__ (line 1777) | def __init__(self, *, method _gen_model_final (line 1849) | def _gen_model_final(self): method fit (line 1860) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, groups=N... method fit_cate_intercept_ (line 1905) | def fit_cate_intercept_(self): method bias_part_of_coef (line 1909) | def bias_part_of_coef(self): method model_final (line 1913) | def model_final(self): method model_final (line 1917) | def model_final(self, model): class ForestDRIV (line 1922) | class ForestDRIV(ForestModelFinalCateEstimatorMixin, DRIV): method __init__ (line 2226) | def __init__(self, *, method _gen_model_final (line 2308) | def _gen_model_final(self): method fit (line 2326) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, groups=N... method model_final (line 2369) | def model_final(self): method model_final (line 2373) | def model_final(self, model): class _IntentToTreatDRIVNuisanceSelector (line 2378) | class _IntentToTreatDRIVNuisanceSelector(ModelSelector): method __init__ (line 2379) | def __init__(self, method train (line 2389) | def train(self, is_selecting, folds, Y, T, X=None, W=None, Z=None, sam... method score (line 2401) | def score(self, Y, T, X=None, W=None, Z=None, sample_weight=None, grou... method predict (line 2422) | def predict(self, Y, T, X=None, W=None, Z=None, sample_weight=None, gr... class _DummyClassifier (line 2451) | class _DummyClassifier: method __init__ (line 2461) | def __init__(self, *, ratio): method fit (line 2464) | def fit(self, X, y, **kwargs): method predict_proba (line 2467) | def predict_proba(self, X): class _IntentToTreatDRIV (line 2472) | class _IntentToTreatDRIV(_BaseDRIV): method __init__ (line 2475) | def __init__(self, *, method _gen_prel_model_effect (line 2516) | def _gen_prel_model_effect(self): method _gen_ortho_learner_model_nuisance (line 2519) | def _gen_ortho_learner_model_nuisance(self): class _DummyCATE (line 2537) | class _DummyCATE: method __init__ (line 2540) | def __init__(self): method fit (line 2543) | def fit(self, y, T, *, Z, X=None, W=None, sample_weight=None, groups=N... method effect (line 2546) | def effect(self, X): class IntentToTreatDRIV (line 2552) | class IntentToTreatDRIV(_IntentToTreatDRIV): method __init__ (line 2707) | def __init__(self, *, method _gen_model_final (line 2757) | def _gen_model_final(self): method _gen_prel_model_effect (line 2762) | def _gen_prel_model_effect(self): method models_y_xw (line 2796) | def models_y_xw(self): method models_t_xwz (line 2810) | def models_t_xwz(self): method models_prel_model_effect (line 2824) | def models_prel_model_effect(self): method nuisance_scores_y_xw (line 2838) | def nuisance_scores_y_xw(self): method nuisance_scores_t_xwz (line 2843) | def nuisance_scores_t_xwz(self): method nuisance_scores_prel_model_effect (line 2848) | def nuisance_scores_prel_model_effect(self): class LinearIntentToTreatDRIV (line 2853) | class LinearIntentToTreatDRIV(StatsModelsCateEstimatorMixin, IntentToTre... method __init__ (line 3021) | def __init__(self, *, method _gen_model_final (line 3066) | def _gen_model_final(self): method fit (line 3070) | def fit(self, Y, T, *, Z, X=None, W=None, sample_weight=None, freq_wei... method fit_cate_intercept_ (line 3117) | def fit_cate_intercept_(self): method bias_part_of_coef (line 3121) | def bias_part_of_coef(self): method model_final (line 3125) | def model_final(self): method model_final (line 3129) | def model_final(self, value): FILE: econml/iv/sieve/_tsls.py class HermiteFeatures (line 18) | class HermiteFeatures(TransformerMixin): method __init__ (line 30) | def __init__(self, degree, shift=0, joint=False): method _column_feats (line 35) | def _column_feats(self, X, shift): method fit (line 48) | def fit(self, X): method transform (line 52) | def transform(self, X): class DPolynomialFeatures (line 89) | class DPolynomialFeatures(TransformerMixin): method __init__ (line 113) | def __init__(self, degree=2, interaction_only=False, include_bias=True): method fit (line 116) | def fit(self, X, y=None): method transform (line 133) | def transform(self, X): function _add_ones (line 160) | def _add_ones(arr): function _add_zeros (line 165) | def _add_zeros(arr): class SieveTSLS (line 170) | class SieveTSLS(BaseCateEstimator): method __init__ (line 195) | def __init__(self, *, method fit (line 211) | def fit(self, Y, T, *, Z, X=None, W=None, inference=None): method effect (line 269) | def effect(self, X=None, T0=0, T1=1): method marginal_effect (line 314) | def marginal_effect(self, T, X=None): FILE: econml/metalearners/_metalearners.py class TLearner (line 22) | class TLearner(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 68) | def __init__(self, *, method _gen_allowed_missing_vars (line 77) | def _gen_allowed_missing_vars(self): method fit (line 81) | def fit(self, Y, T, *, X, inference=None): method const_marginal_effect (line 120) | def const_marginal_effect(self, X): class SLearner (line 150) | class SLearner(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 195) | def __init__(self, *, method _gen_allowed_missing_vars (line 204) | def _gen_allowed_missing_vars(self): method fit (line 208) | def fit(self, Y, T, *, X=None, inference=None): method const_marginal_effect (line 249) | def const_marginal_effect(self, X=None): class XLearner (line 284) | class XLearner(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 342) | def __init__(self, *, method _gen_allowed_missing_vars (line 355) | def _gen_allowed_missing_vars(self): method fit (line 359) | def fit(self, Y, T, *, X, inference=None): method const_marginal_effect (line 418) | def const_marginal_effect(self, X): class DomainAdaptationLearner (line 451) | class DomainAdaptationLearner(TreatmentExpansionMixin, LinearCateEstimat... method __init__ (line 510) | def __init__(self, *, method _gen_allowed_missing_vars (line 523) | def _gen_allowed_missing_vars(self): method fit (line 527) | def fit(self, Y, T, *, X, inference=None): method const_marginal_effect (line 590) | def const_marginal_effect(self, X): method _fit_weighted_pipeline (line 618) | def _fit_weighted_pipeline(self, model_instance, X, y, sample_weight): method shap_values (line 625) | def shap_values(self, X, *, feature_names=None, treatment_names=None, ... FILE: econml/orf/_causal_tree.py class Node (line 14) | class Node: method __init__ (line 27) | def __init__(self, sample_inds, estimate_inds): method find_tree_node (line 35) | def find_tree_node(self, value): class CausalTree (line 52) | class CausalTree: method __init__ (line 94) | def __init__(self, method create_splits (line 109) | def create_splits(self, Y, T, X, W, method print_tree_rec (line 259) | def print_tree_rec(self, node): method print_tree (line 268) | def print_tree(self): method find_split (line 271) | def find_split(self, value): FILE: econml/orf/_ortho_forest.py function _build_tree_in_parallel (line 46) | def _build_tree_in_parallel(tree, Y, T, X, W, function _fit_weighted_pipeline (line 53) | def _fit_weighted_pipeline(model_instance, X, y, sample_weight): function _cross_fit (line 79) | def _cross_fit(model_instance, X, y, split_indices, sample_weight=None, ... function _group_predict (line 100) | def _group_predict(X, n_groups, predict_func): function _group_cross_fit (line 127) | def _group_cross_fit(model_instance, X, y, t, split_indices, sample_weig... function _pointwise_effect (line 152) | def _pointwise_effect(X_single, Y, T, X, W, w_nonzero, split_inds, slice... class BaseOrthoForest (line 199) | class BaseOrthoForest(TreatmentExpansionMixin, LinearCateEstimator): method __init__ (line 202) | def __init__(self, method _gen_allowed_missing_vars (line 254) | def _gen_allowed_missing_vars(self): method fit (line 258) | def fit(self, Y, T, *, X, W=None, inference='auto'): method const_marginal_effect (line 310) | def const_marginal_effect(self, X): method _predict (line 327) | def _predict(self, X, stderr=False): method _pw_effect_inputs (line 340) | def _pw_effect_inputs(self, X_single, stderr=False): method _get_inference_options (line 366) | def _get_inference_options(self): method _fit_forest (line 374) | def _fit_forest(self, Y, T, X, W=None): method _get_weights (line 390) | def _get_weights(self, X_single, tree_slice=None): method _get_blb_indices (line 425) | def _get_blb_indices(self, X): class DMLOrthoForest (line 447) | class DMLOrthoForest(BaseOrthoForest): method __init__ (line 545) | def __init__(self, *, method _combine (line 632) | def _combine(self, X, W): method fit (line 641) | def fit(self, Y, T, *, X, W=None, inference='auto'): method const_marginal_effect (line 701) | def const_marginal_effect(self, X): class _DMLOrthoForest_nuisance_estimator_generator (line 709) | class _DMLOrthoForest_nuisance_estimator_generator: method __init__ (line 712) | def __init__(self, model_T, model_Y, random_state=None, second_stage=T... method __call__ (line 721) | def __call__(self, Y, T, X, W, sample_weight=None, split_indices=None): function _DMLOrthoForest_parameter_estimator_func (line 767) | def _DMLOrthoForest_parameter_estimator_func(Y, T, X, class _DMLOrthoForest_second_stage_parameter_estimator_gen (line 781) | class _DMLOrthoForest_second_stage_parameter_estimator_gen: method __init__ (line 789) | def __init__(self, lambda_reg): method __call__ (line 792) | def __call__(self, Y, T, X, function _DMLOrthoForest_moment_and_mean_gradient_estimator_func (line 824) | def _DMLOrthoForest_moment_and_mean_gradient_estimator_func(Y, T, X, W, function _DMLOrthoForest_get_conforming_residuals (line 839) | def _DMLOrthoForest_get_conforming_residuals(Y, T, nuisance_estimates): class DROrthoForest (line 849) | class DROrthoForest(BaseOrthoForest): method __init__ (line 933) | def __init__(self, *, method fit (line 993) | def fit(self, Y, T, *, X, W=None, inference='auto'): method const_marginal_effect (line 1045) | def const_marginal_effect(self, X): method const_marginal_ate (line 1064) | def const_marginal_ate(self, X=None): method nuisance_estimator_generator (line 1084) | def nuisance_estimator_generator(propensity_model, model_Y, random_sta... method parameter_estimator_func (line 1131) | def parameter_estimator_func(Y, T, X, method second_stage_parameter_estimator_gen (line 1142) | def second_stage_parameter_estimator_gen(lambda_reg): method moment_and_mean_gradient_estimator_func (line 1183) | def moment_and_mean_gradient_estimator_func(Y, T, X, W, method _partial_moments (line 1199) | def _partial_moments(Y, T, nuisance_estimates): method _check_treatment (line 1212) | def _check_treatment(self, T): class BLBInference (line 1226) | class BLBInference(Inference): method fit (line 1238) | def fit(self, estimator, *args, **kwargs): method const_marginal_effect_interval (line 1255) | def const_marginal_effect_interval(self, X=None, *, alpha=0.05): method const_marginal_effect_inference (line 1290) | def const_marginal_effect_inference(self, X=None): method _effect_inference_helper (line 1321) | def _effect_inference_helper(self, X, T0, T1): method effect_interval (line 1332) | def effect_interval(self, X=None, *, T0=0, T1=1, alpha=0.05): method effect_inference (line 1363) | def effect_inference(self, X=None, *, T0=0, T1=1): method _marginal_effect_inference_helper (line 1395) | def _marginal_effect_inference_helper(self, T, X): method marginal_effect_inference (line 1452) | def marginal_effect_inference(self, T, X): method marginal_effect_interval (line 1466) | def marginal_effect_interval(self, T, X, *, alpha=0.05): method _predict_wrapper (line 1469) | def _predict_wrapper(self, X=None): FILE: econml/panel/dml/_dml.py function _get_groups_period_filter (line 18) | def _get_groups_period_filter(groups, n_periods): class _DynamicModelNuisanceSelector (line 29) | class _DynamicModelNuisanceSelector(ModelSelector): method __init__ (line 38) | def __init__(self, model_y, model_t, n_periods): method train (line 43) | def train(self, is_selecting, folds, Y, T, X=None, W=None, sample_weig... method predict (line 99) | def predict(self, Y, T, X=None, W=None, sample_weight=None, groups=None): method score (line 137) | def score(self, Y, T, X=None, W=None, sample_weight=None, groups=None): method _get_shape_formatter (line 162) | def _get_shape_formatter(self, X, W): method _index_or_None (line 167) | def _index_or_None(self, X, filter_idx): class _DynamicModelFinal (line 171) | class _DynamicModelFinal: method __init__ (line 188) | def __init__(self, model_final, n_periods): method fit (line 193) | def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_wei... method predict (line 212) | def predict(self, X=None): method score (line 229) | def score(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_w... class _LinearDynamicModelFinal (line 253) | class _LinearDynamicModelFinal(_DynamicModelFinal): method __init__ (line 260) | def __init__(self, model_final, n_periods): method fit (line 264) | def fit(self, Y, T, X=None, W=None, Z=None, nuisances=None, sample_wei... method _get_coef_ (line 274) | def _get_coef_(self): method _get_cov (line 283) | def _get_cov(self, nuisances, X, groups): method _fit_single_output_cov (line 291) | def _fit_single_output_cov(self, nuisances, X, y_index, groups): class _DynamicFinalWrapper (line 349) | class _DynamicFinalWrapper(_FinalWrapper): method predict_with_res (line 351) | def predict_with_res(self, X, T_res): class DynamicDML (line 359) | class DynamicDML(LinearModelFinalCateEstimatorMixin, _OrthoLearner): method __init__ (line 496) | def __init__(self, *, method _gen_allowed_missing_vars (line 527) | def _gen_allowed_missing_vars(self): method const_marginal_effect (line 531) | def const_marginal_effect(self, X=None): method const_marginal_ate (line 554) | def const_marginal_ate(self, X=None): method _gen_featurizer (line 573) | def _gen_featurizer(self): method _gen_model_y (line 576) | def _gen_model_y(self): method _gen_model_t (line 581) | def _gen_model_t(self): method _gen_model_final (line 586) | def _gen_model_final(self): method _gen_ortho_learner_model_nuisance (line 589) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 595) | def _gen_ortho_learner_model_final(self): method _prefit (line 603) | def _prefit(self, Y, T, *args, groups=None, only_final=False, **kwargs): method _postfit (line 613) | def _postfit(self, Y, T, *args, **kwargs): method _strata (line 618) | def _strata(self, Y, T, X=None, W=None, Z=None, method fit (line 624) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, sample_var=... method score (line 684) | def score(self, Y, T, X=None, W=None, sample_weight=None, *, groups): method cate_treatment_names (line 742) | def cate_treatment_names(self, treatment_names=None): method cate_feature_names (line 765) | def cate_feature_names(self, feature_names=None): method _expand_treatments (line 792) | def _expand_treatments(self, X, *Ts, transform=True): method bias_part_of_coef (line 813) | def bias_part_of_coef(self): method fit_cate_intercept_ (line 817) | def fit_cate_intercept_(self): method original_featurizer (line 821) | def original_featurizer(self): method featurizer_ (line 827) | def featurizer_(self): method model_final_ (line 833) | def model_final_(self): method model_final (line 839) | def model_final(self): method model_final (line 843) | def model_final(self, model): method models_y (line 848) | def models_y(self): method models_t (line 852) | def models_t(self): method nuisance_scores_y (line 856) | def nuisance_scores_y(self): method nuisance_scores_t (line 860) | def nuisance_scores_t(self): method residuals_ (line 864) | def residuals_(self): FILE: econml/panel/utilities.py function long (line 5) | def long(x): function wide (line 27) | def wide(x): FILE: econml/policy/_base.py class PolicyLearner (line 9) | class PolicyLearner(metaclass=abc.ABCMeta): method fit (line 11) | def fit(self, Y, T, *, X=None, **kwargs): method predict_value (line 14) | def predict_value(self, X): method predict (line 17) | def predict(self, X): FILE: econml/policy/_drlearner.py class _PolicyModelFinal (line 14) | class _PolicyModelFinal(_ModelFinal): method fit (line 16) | def fit(self, Y, T, X=None, W=None, *, nuisances, method predict (line 34) | def predict(self, X=None): method score (line 42) | def score(self, Y, T, X=None, W=None, *, nuisances, sample_weight=None... class _DRLearnerWrapper (line 46) | class _DRLearnerWrapper(DRLearner): method _gen_ortho_learner_model_final (line 48) | def _gen_ortho_learner_model_final(self): class _BaseDRPolicyLearner (line 52) | class _BaseDRPolicyLearner(PolicyLearner): method _gen_drpolicy_learner (line 54) | def _gen_drpolicy_learner(self): method fit (line 57) | def fit(self, Y, T, *, X=None, W=None, sample_weight=None, groups=None): method predict_value (line 86) | def predict_value(self, X): method predict_proba (line 102) | def predict_proba(self, X): method predict (line 120) | def predict(self, X): method policy_feature_names (line 139) | def policy_feature_names(self, *, feature_names=None): method policy_treatment_names (line 156) | def policy_treatment_names(self, *, treatment_names=None): method feature_importances (line 179) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 201) | def feature_importances_(self): method policy_model_ (line 205) | def policy_model_(self): class DRPolicyTree (line 210) | class DRPolicyTree(_BaseDRPolicyLearner): method __init__ (line 384) | def __init__(self, *, method _gen_drpolicy_learner (line 420) | def _gen_drpolicy_learner(self): method plot (line 441) | def plot(self, *, feature_names=None, treatment_names=None, ax=None, t... method export_graphviz (line 489) | def export_graphviz(self, *, out_file=None, method render (line 546) | def render(self, out_file, *, format='pdf', view=True, feature_names=N... class DRPolicyForest (line 609) | class DRPolicyForest(_BaseDRPolicyLearner): method __init__ (line 804) | def __init__(self, *, method _gen_drpolicy_learner (line 848) | def _gen_drpolicy_learner(self): method plot (line 873) | def plot(self, tree_id, *, feature_names=None, treatment_names=None, method export_graphviz (line 927) | def export_graphviz(self, tree_id, *, out_file=None, feature_names=Non... method render (line 989) | def render(self, tree_id, out_file, *, format='pdf', view=True, FILE: econml/policy/_forest/_forest.py class PolicyForest (line 34) | class PolicyForest(BaseEnsemble, metaclass=ABCMeta): method __init__ (line 161) | def __init__(self, method apply (line 201) | def apply(self, X): method decision_path (line 224) | def decision_path(self, X): method fit (line 255) | def fit(self, X, y, *, sample_weight=None, **kwargs): method get_subsample_inds (line 374) | def get_subsample_inds(self,): method feature_importances (line 381) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 414) | def feature_importances_(self): method _validate_X_predict (line 417) | def _validate_X_predict(self, X): method predict_value (line 423) | def predict_value(self, X): method predict_proba (line 457) | def predict_proba(self, X): method predict (line 495) | def predict(self, X): FILE: econml/policy/_forest/_tree.py class PolicyTree (line 31) | class PolicyTree(_SingleTreeExporterMixin, BaseTree): method __init__ (line 163) | def __init__(self, *, method _get_valid_criteria (line 187) | def _get_valid_criteria(self): method _get_store_jac (line 190) | def _get_store_jac(self): method init (line 193) | def init(self,): method fit (line 196) | def fit(self, X, y, *, sample_weight=None, check_input=True): method predict (line 233) | def predict(self, X, check_input=True): method predict_proba (line 255) | def predict_proba(self, X, check_input=True): method predict_value (line 279) | def predict_value(self, X, check_input=True): method feature_importances (line 301) | def feature_importances(self, max_depth=4, depth_decay_exponent=2.0): method feature_importances_ (line 323) | def feature_importances_(self): method _make_dot_exporter (line 326) | def _make_dot_exporter(self, *, out_file, feature_names, treatment_nam... method _make_mpl_exporter (line 339) | def _make_mpl_exporter(self, *, title, feature_names, treatment_names,... FILE: econml/score/ensemble_cate.py class EnsembleCateEstimator (line 9) | class EnsembleCateEstimator: method __init__ (line 28) | def __init__(self, *, cate_models, weights): method effect (line 32) | def effect(self, X=None, *, T0=0, T1=1): method marginal_effect (line 37) | def marginal_effect(self, T, X=None): method const_marginal_effect (line 42) | def const_marginal_effect(self, X=None): method cate_models (line 51) | def cate_models(self): method cate_models (line 55) | def cate_models(self, value): method weights (line 61) | def weights(self): method weights (line 65) | def weights(self, value): FILE: econml/score/rscorer.py class RScorer (line 11) | class RScorer: method __init__ (line 106) | def __init__(self, *, method fit (line 126) | def fit(self, y, T, X=None, W=None, sample_weight=None, groups=None): method score (line 169) | def score(self, cate_model): method best_model (line 196) | def best_model(self, cate_models, return_scores=False): method ensemble (line 222) | def ensemble(self, cate_models, eta=1000.0, return_scores=False): FILE: econml/sklearn_extensions/linear_model.py class _WeightedCVIterableWrapper (line 42) | class _WeightedCVIterableWrapper(_CVIterableWrapper): method __init__ (line 43) | def __init__(self, cv): method get_n_splits (line 46) | def get_n_splits(self, X=None, y=None, groups=None, sample_weight=None): method split (line 51) | def split(self, X=None, y=None, groups=None, sample_weight=None): function _weighted_check_cv (line 57) | def _weighted_check_cv(cv=5, y=None, classifier=False, random_state=None): class WeightedModelMixin (line 78) | class WeightedModelMixin: method _fit_weighted_linear_model (line 84) | def _fit_weighted_linear_model(self, X, y, sample_weight, check_input=... class WeightedLasso (line 137) | class WeightedLasso(WeightedModelMixin, Lasso): method __init__ (line 208) | def __init__(self, alpha=1.0, fit_intercept=True, method fit (line 219) | def fit(self, X, y, sample_weight=None, check_input=True): class WeightedMultiTaskLasso (line 242) | class WeightedMultiTaskLasso(WeightedModelMixin, MultiTaskLasso): method __init__ (line 304) | def __init__(self, alpha=1.0, fit_intercept=True, method fit (line 312) | def fit(self, X, y, sample_weight=None): class WeightedLassoCV (line 331) | class WeightedLassoCV(WeightedModelMixin, LassoCV): method __init__ (line 413) | def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=... method fit (line 425) | def fit(self, X, y, sample_weight=None): class WeightedMultiTaskLassoCV (line 449) | class WeightedMultiTaskLassoCV(WeightedModelMixin, MultiTaskLassoCV): method __init__ (line 524) | def __init__(self, eps=1e-3, n_alphas=100, alphas=None, fit_intercept=... method fit (line 536) | def fit(self, X, y, sample_weight=None): function _get_theta_coefs_and_tau_sq (line 560) | def _get_theta_coefs_and_tau_sq(i, X, sample_weight, alpha_cov, n_alphas... class DebiasedLasso (line 588) | class DebiasedLasso(WeightedLasso): method __init__ (line 689) | def __init__(self, alpha='auto', n_alphas=100, alpha_cov='auto', n_alp... method fit (line 704) | def fit(self, X, y, sample_weight=None, check_input=True): method prediction_stderr (line 777) | def prediction_stderr(self, X): method predict_interval (line 800) | def predict_interval(self, X, alpha=0.05): method coef__interval (line 830) | def coef__interval(self, alpha=0.05): method intercept__interval (line 849) | def intercept__interval(self, alpha=0.05): method _get_coef_correction (line 871) | def _get_coef_correction(self, X, y, y_pred, sample_weight, theta_hat): method _get_optimal_alpha (line 884) | def _get_optimal_alpha(self, X, y, sample_weight): method _get_theta_hat (line 895) | def _get_theta_hat(self, X, sample_weight): method _get_unscaled_coef_var (line 922) | def _get_unscaled_coef_var(self, X, theta_hat, sample_weight): class MultiOutputDebiasedLasso (line 933) | class MultiOutputDebiasedLasso(MultiOutputRegressor): method __init__ (line 1025) | def __init__(self, alpha='auto', n_alphas=100, alpha_cov='auto', n_alp... method fit (line 1038) | def fit(self, X, y, sample_weight=None): method predict (line 1075) | def predict(self, X): method prediction_stderr (line 1094) | def prediction_stderr(self, X): method predict_interval (line 1116) | def predict_interval(self, X, alpha=0.05): method coef__interval (line 1144) | def coef__interval(self, alpha=0.05): method intercept__interval (line 1168) | def intercept__interval(self, alpha=0.05): method get_params (line 1188) | def get_params(self, deep=True): method set_params (line 1192) | def set_params(self, **params): method _set_attribute (line 1196) | def _set_attribute(self, attribute_name, condition=True, default=None): class _PairedEstimatorWrapper (line 1207) | class _PairedEstimatorWrapper: method __init__ (line 1219) | def __init__(self, *args, **kwargs): method fit (line 1225) | def fit(self, X, y, sample_weight=None): method predict (line 1242) | def predict(self, X): method score (line 1246) | def score(self, X, y, sample_weight=None): method __getattr__ (line 1249) | def __getattr__(self, key): method __setattr__ (line 1255) | def __setattr__(self, key, value): method get_params (line 1261) | def get_params(self, deep=True): method set_params (line 1265) | def set_params(self, **params): class WeightedLassoCVWrapper (line 1270) | class WeightedLassoCVWrapper(_PairedEstimatorWrapper): class WeightedLassoWrapper (line 1285) | class WeightedLassoWrapper(_PairedEstimatorWrapper): class SelectiveRegularization (line 1295) | class SelectiveRegularization: method __init__ (line 1353) | def __init__(self, unpenalized_inds, penalized_model, fit_intercept=Tr... method fit (line 1358) | def fit(self, X, y, sample_weight=None): method predict (line 1420) | def predict(self, X): method score (line 1439) | def score(self, X, y): method __getattr__ (line 1462) | def __getattr__(self, key): method __setattr__ (line 1477) | def __setattr__(self, key, value): class _StatsModelsWrapper (line 1484) | class _StatsModelsWrapper(BaseEstimator): method predict (line 1501) | def predict(self, X): method coef_ (line 1522) | def coef_(self): method intercept_ (line 1545) | def intercept_(self): method _param_stderr (line 1558) | def _param_stderr(self): method coef_stderr_ (line 1573) | def coef_stderr_(self): method intercept_stderr_ (line 1585) | def intercept_stderr_(self): method prediction_stderr (line 1596) | def prediction_stderr(self, X): method coef__interval (line 1620) | def coef__interval(self, alpha=0.05): method intercept__interval (line 1638) | def intercept__interval(self, alpha=0.05): method predict_interval (line 1660) | def predict_interval(self, X, alpha=0.05): class StatsModelsLinearRegression (line 1684) | class StatsModelsLinearRegression(_StatsModelsWrapper): method __init__ (line 1702) | def __init__(self, fit_intercept=True, cov_type="HC0", *, enable_feder... method _check_input (line 1707) | def _check_input(self, X, y, sample_weight, freq_weight, sample_var): method fit (line 1772) | def fit(self, X, y, sample_weight=None, freq_weight=None, sample_var=N... method aggregate (line 1882) | def aggregate(models: List[StatsModelsLinearRegression]): class StatsModelsRLM (line 1958) | class StatsModelsRLM(_StatsModelsWrapper): method __init__ (line 1977) | def __init__(self, t=1.345, method _check_input (line 1989) | def _check_input(self, X, y): method fit (line 1998) | def fit(self, X, y): class StatsModels2SLS (line 2037) | class StatsModels2SLS(_StatsModelsWrapper): method __init__ (line 2047) | def __init__(self, cov_type="HC0"): method _check_input (line 2052) | def _check_input(self, Z, T, y, sample_weight): method fit (line 2078) | def fit(self, Z, T, y, sample_weight=None, freq_weight=None, sample_va... FILE: econml/sklearn_extensions/model_selection.py function _split_weighted_sample (line 37) | def _split_weighted_sample(self, X, y, sample_weight, is_stratified=False): class WeightedKFold (line 101) | class WeightedKFold: method __init__ (line 130) | def __init__(self, n_splits=3, n_trials=10, shuffle=False, random_stat... method split (line 136) | def split(self, X, y, sample_weight=None): method get_n_splits (line 153) | def get_n_splits(self, X, y, groups=None): method _get_folds_from_splits (line 174) | def _get_folds_from_splits(self, splits, sample_size): method _get_splits_from_weight_stratification (line 181) | def _get_splits_from_weight_stratification(self, sample_weight): class WeightedStratifiedKFold (line 204) | class WeightedStratifiedKFold(WeightedKFold): method split (line 233) | def split(self, X, y, sample_weight=None): method get_n_splits (line 250) | def get_n_splits(self, X, y, groups=None): class ModelSelector (line 272) | class ModelSelector(metaclass=abc.ABCMeta): method train (line 281) | def train(self, is_selecting: bool, folds: Optional[List], *args, **kw... method predict (line 289) | def predict(self, *args, **kwargs): method score (line 298) | def score(self, *args, **kwargs): class SingleModelSelector (line 308) | class SingleModelSelector(ModelSelector): method best_model (line 317) | def best_model(self): method best_score (line 322) | def best_score(self): method predict (line 325) | def predict(self, *args, **kwargs): method __getattr__ (line 330) | def __getattr__(self, name): method score (line 336) | def score(self, *args, **kwargs): function _fit_with_groups (line 343) | def _fit_with_groups(model, X, y, *, sub_model=None, groups, **kwargs): class FixedModelSelector (line 381) | class FixedModelSelector(SingleModelSelector): method __init__ (line 384) | def __init__(self, model, score_during_selection): method train (line 388) | def train(self, is_selecting, folds: Optional[List], X, y, groups=None... method best_model (line 410) | def best_model(self): method best_score (line 414) | def best_score(self): function _copy_to (line 421) | def _copy_to(m1, m2, attrs, insert_underscore=False): function _convert_linear_model (line 426) | def _convert_linear_model(model, new_cls): function _to_logisticRegression (line 435) | def _to_logisticRegression(model: LogisticRegressionCV): function _convert_linear_regression (line 457) | def _convert_linear_regression(model, new_cls, extra_attrs=["positive"]): function _to_elasticNet (line 463) | def _to_elasticNet(model: ElasticNetCV, args, kwargs, is_lasso=False, cl... function _to_ridge (line 480) | def _to_ridge(model, cls=Ridge, extra_attrs=["positive"]): class SklearnCVSelector (line 486) | class SklearnCVSelector(SingleModelSelector): method __init__ (line 489) | def __init__(self, searcher): method convertible_types (line 493) | def convertible_types(): method can_wrap (line 497) | def can_wrap(model): method _model_mapping (line 503) | def _model_mapping(): method _convert_model (line 522) | def _convert_model(model, args, kwargs): method train (line 536) | def train(self, is_selecting: bool, folds: Optional[List], *args, grou... method best_model (line 562) | def best_model(self): method best_score (line 566) | def best_score(self): class ListSelector (line 570) | class ListSelector(SingleModelSelector): method __init__ (line 582) | def __init__(self, models, unwrap=True): method train (line 586) | def train(self, is_selecting, folds: Optional[List], *args, **kwargs): method best_model (line 601) | def best_model(self): method best_score (line 611) | def best_score(self): function get_selector (line 615) | def get_selector(input, is_discrete, *, random_state=None, cv=None, wrap... class GridSearchCVList (line 655) | class GridSearchCVList(BaseEstimator): method __init__ (line 681) | def __init__(self, estimator_list, param_grid_list, scoring=None, method fit (line 696) | def fit(self, X, y=None, **fit_params): method predict (line 708) | def predict(self, X): method predict_proba (line 711) | def predict_proba(self, X): function _cross_val_predict (line 715) | def _cross_val_predict(estimator, X, y=None, *, groups=None, cv=None, FILE: econml/solutions/causal_analysis/_causal_analysis.py class _CausalInsightsConstants (line 40) | class _CausalInsightsConstants: function _get_default_shared_insights_output (line 76) | def _get_default_shared_insights_output(): function _get_default_specific_insights (line 96) | def _get_default_specific_insights(view): function _get_metadata_causal_insights_keys (line 109) | def _get_metadata_causal_insights_keys(): function _get_column_causal_insights_keys (line 116) | def _get_column_causal_insights_keys(): function _get_data_causal_insights_keys (line 123) | def _get_data_causal_insights_keys(): function _first_stage_reg (line 132) | def _first_stage_reg(X, y, *, automl=True, random_state=None, verbose=0): function _first_stage_clf (line 155) | def _first_stage_clf(X, y, *, make_regressor=False, automl=True, min_cou... function _final_stage (line 187) | def _final_stage(*, random_state=None, verbose=0): class _ColumnTransformer (line 200) | class _ColumnTransformer(TransformerMixin): method __init__ (line 201) | def __init__(self, categorical, passthrough): method fit (line 205) | def fit(self, X): method transform (line 216) | def transform(self, X): method get_feature_names (line 227) | def get_feature_names(self, names=None): method get_feature_names_out (line 230) | def get_feature_names_out(self, names=None): class _Wrapper (line 242) | class _Wrapper: method __init__ (line 243) | def __init__(self, item): class _FrozenTransformer (line 247) | class _FrozenTransformer(TransformerMixin, BaseEstimator): method __init__ (line 248) | def __init__(self, wrapper): method fit (line 251) | def fit(self, X, y): method transform (line 254) | def transform(self, X): function _freeze (line 258) | def _freeze(transformer): function _sanitize (line 263) | def _sanitize(obj): function _tree_interpreter_to_dict (line 276) | def _tree_interpreter_to_dict(interp, features, leaf_data=lambda t, n: {}): class _PolicyOutput (line 291) | class _PolicyOutput: method __init__ (line 307) | def __init__(self, tree_dictionary, policy_value, always_treat, contro... function _process_feature (line 321) | def _process_feature(name, feat_ind, verbose, categorical_inds, categori... class CausalAnalysis (line 512) | class CausalAnalysis: method __init__ (line 611) | def __init__(self, feature_inds, categorical, heterogeneity_inds=None,... method fit (line 631) | def fit(self, X, y, warm_start=False): method _format_col (line 908) | def _format_col(self, ind): method _point_props (line 916) | def _point_props(alpha): method _summary_props (line 926) | def _summary_props(alpha): method _make_accessor (line 937) | def _make_accessor(attr): method _summarize (line 952) | def _summarize(self, *, summary, get_inference, props, expand_arr, dro... method _pandas_summary (line 988) | def _pandas_summary(self, get_inference, *, props, n, method _dict_summary (line 1042) | def _dict_summary(self, get_inference, *, n, props, kind, drop_sample=... method global_causal_effect (line 1102) | def global_causal_effect(self, *, alpha=0.05, keep_all_levels=False): method _global_causal_effect_dict (line 1132) | def _global_causal_effect_dict(self, *, alpha=0.05, row_wise=False): method _cohort_effect_inference (line 1143) | def _cohort_effect_inference(self, Xtest): method cohort_causal_effect (line 1157) | def cohort_causal_effect(self, Xtest, *, alpha=0.05, keep_all_levels=F... method _cohort_causal_effect_dict (line 1189) | def _cohort_causal_effect_dict(self, Xtest, *, alpha=0.05, row_wise=Fa... method _local_effect_inference (line 1200) | def _local_effect_inference(self, Xtest): method local_causal_effect (line 1218) | def local_causal_effect(self, Xtest, *, alpha=0.05, keep_all_levels=Fa... method _local_causal_effect_dict (line 1252) | def _local_causal_effect_dict(self, Xtest, *, alpha=0.05, row_wise=Fal... method _safe_result_index (line 1263) | def _safe_result_index(self, X, feature_index): method _whatif_inference (line 1298) | def _whatif_inference(self, X, Xnew, feature_index, y): method whatif (line 1321) | def whatif(self, X, Xnew, feature_index, y, *, alpha=0.05): method _whatif_dict (line 1349) | def _whatif_dict(self, X, Xnew, feature_index, y, *, alpha=0.05, row_w... method _tree (line 1398) | def _tree(self, is_policy, Xtest, feature_index, *, treatment_costs=0, method plot_policy_tree (line 1456) | def plot_policy_tree(self, Xtest, feature_index, *, treatment_costs=0, method _policy_tree_output (line 1493) | def _policy_tree_output(self, Xtest, feature_index, *, treatment_costs=0, method plot_heterogeneity_tree (line 1547) | def plot_heterogeneity_tree(self, Xtest, feature_index, *, method _heterogeneity_tree_output (line 1582) | def _heterogeneity_tree_output(self, Xtest, feature_index, *, method individualized_policy (line 1622) | def individualized_policy(self, Xtest, feature_index, *, n_rows=None, ... method _individualized_policy_dict (line 1751) | def _individualized_policy_dict(self, Xtest, feature_index, *, n_rows=... method typical_treatment_value (line 1779) | def typical_treatment_value(self, feature_index): FILE: econml/tests/dgp.py class _BaseDynamicPanelDGP (line 19) | class _BaseDynamicPanelDGP: method __init__ (line 21) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 28) | def create_instance(self, *args, **kwargs): method _gen_data_with_policy (line 32) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method static_policy_data (line 35) | def static_policy_data(self, n_units, tau, random_seed=123): method adaptive_policy_data (line 40) | def adaptive_policy_data(self, n_units, policy_gen, random_seed=123): method static_policy_effect (line 43) | def static_policy_effect(self, tau, mc_samples=1000): method adaptive_policy_effect (line 51) | def adaptive_policy_effect(self, policy_gen, mc_samples=1000): class DynamicPanelDGP (line 60) | class DynamicPanelDGP(_BaseDynamicPanelDGP): method __init__ (line 62) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 65) | def create_instance(self, s_x, sigma_x=.8, sigma_y=.1, conf_str=5, het... method hetero_effect_fn (line 125) | def hetero_effect_fn(self, t, x): method _gen_data_with_policy (line 133) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method observational_data (line 160) | def observational_data(self, n_units, gamma=0, s_t=1, sigma_t=0.5, ran... function add_vlines (line 181) | def add_vlines(n_periods, n_treatments, hetero_inds): FILE: econml/tests/test_ate_inference.py class TestATEInference (line 12) | class TestATEInference(unittest.TestCase): method setUpClass (line 15) | def setUpClass(cls): method test_ate_inference (line 27) | def test_ate_inference(self): FILE: econml/tests/test_automated_ml.py function automl_model_reg (line 80) | def automl_model_reg(): function automl_model_clf (line 83) | def automl_model_clf(): function automl_model_linear_reg (line 88) | def automl_model_linear_reg(): function automl_model_sample_weight_reg (line 93) | def automl_model_sample_weight_reg(): class TestAutomatedML (line 103) | class TestAutomatedML(unittest.TestCase): method setUpClass (line 106) | def setUpClass(cls): method test_nonparam (line 117) | def test_nonparam(self): method test_param (line 126) | def test_param(self): method test_forest_dml (line 135) | def test_forest_dml(self): method test_TLearner (line 148) | def test_TLearner(self): method test_SLearner (line 159) | def test_SLearner(self): method test_DALearner (line 171) | def test_DALearner(self): method test_positional (line 181) | def test_positional(self): FILE: econml/tests/test_bootstrap.py class TestBootstrap (line 16) | class TestBootstrap(unittest.TestCase): method test_with_sklearn (line 18) | def test_with_sklearn(self): method test_with_econml (line 77) | def test_with_econml(self): method test_backends (line 131) | def test_backends(self): method test_internal (line 191) | def test_internal(self): method test_internal_options (line 228) | def test_internal_options(self): method test_stratify (line 271) | def test_stratify(self): method test_stratify_orthoiv (line 287) | def test_stratify_orthoiv(self): method test_all_kinds (line 299) | def test_all_kinds(self): FILE: econml/tests/test_cate_interpreter.py class TestCateInterpreter (line 23) | class TestCateInterpreter(unittest.TestCase): method test_can_use_interpreters (line 26) | def test_can_use_interpreters(self): method coinflip (line 46) | def coinflip(p_true=0.5): method test_cate_uncertainty_needs_inference (line 49) | def test_cate_uncertainty_needs_inference(self): method test_can_assign_treatment (line 70) | def test_can_assign_treatment(self): method test_random_cate_settings (line 93) | def test_random_cate_settings(self): FILE: econml/tests/test_causal_analysis.py function assert_less_close (line 16) | def assert_less_close(arr1, arr2): class TestCausalAnalysis (line 21) | class TestCausalAnalysis(unittest.TestCase): method test_basic_array (line 23) | def test_basic_array(self): method test_basic_pandas (line 130) | def test_basic_pandas(self): method test_automl_first_stage (line 248) | def test_automl_first_stage(self): method test_one_feature (line 349) | def test_one_feature(self): method test_final_models (line 410) | def test_final_models(self): method test_forest_with_pandas (line 467) | def test_forest_with_pandas(self): method test_warm_start (line 544) | def test_warm_start(self): method test_empty_hinds (line 569) | def test_empty_hinds(self): method test_can_serialize (line 592) | def test_can_serialize(self): method test_over_cat_limit (line 609) | def test_over_cat_limit(self): method test_individualized_policy (line 649) | def test_individualized_policy(self): method test_random_state (line 680) | def test_random_state(self): method test_can_set_categories (line 708) | def test_can_set_categories(self): method test_policy_with_index (line 727) | def test_policy_with_index(self): method test_invalid_inds (line 737) | def test_invalid_inds(self): method test_scaling_transforms (line 807) | def test_scaling_transforms(self): FILE: econml/tests/test_discrete_outcome.py class TestDiscreteOutcome (line 17) | class TestDiscreteOutcome(unittest.TestCase): method test_accuracy (line 19) | def test_accuracy(self): method test_accuracy_iv (line 64) | def test_accuracy_iv(self): method test_string_outcome (line 99) | def test_string_outcome(self): method test_basic_functionality (line 109) | def test_basic_functionality(self): method test_constraints (line 196) | def test_constraints(self): FILE: econml/tests/test_dml.py function rand_sol (line 38) | def rand_sol(A, b): class TestDML (line 47) | class TestDML(unittest.TestCase): method test_cate_api_without_ray (line 49) | def test_cate_api_without_ray(self): method test_cate_api_with_ray (line 54) | def test_cate_api_with_ray(self): method _test_cate_api (line 62) | def _test_cate_api(self, treatment_featurizations, use_ray=False): method test_cate_api_nonparam_without_ray (line 409) | def test_cate_api_nonparam_without_ray(self): method test_cate_api_nonparam_with_ray (line 413) | def test_cate_api_nonparam_with_ray(self): method _test_cate_api_nonparam (line 420) | def _test_cate_api_nonparam(self, use_ray=False): method test_bad_splits_discrete (line 597) | def test_bad_splits_discrete(self): method test_bad_treatment_nonparam (line 612) | def test_bad_treatment_nonparam(self): method test_access_to_internal_models (line 631) | def test_access_to_internal_models(self): method test_forest_dml_perf (line 668) | def test_forest_dml_perf(self): method test_forest_dml_score_fns (line 747) | def test_forest_dml_score_fns(self): method test_aaforest_pandas (line 822) | def test_aaforest_pandas(self): method test_cfdml_ate_inference (line 838) | def test_cfdml_ate_inference(self): method test_can_use_vectors (line 926) | def test_can_use_vectors(self): method test_can_use_sample_weights (line 938) | def test_can_use_sample_weights(self): method test_discrete_treatments (line 949) | def test_discrete_treatments(self): method _test_can_custom_splitter (line 976) | def _test_can_custom_splitter(self, use_ray=False): method test_can_use_custom_splitter_with_ray (line 990) | def test_can_use_custom_splitter_with_ray(self): method test_can_use_custom_splitter_without_ray (line 997) | def test_can_use_custom_splitter_without_ray(self): method test_can_use_featurizer (line 1000) | def test_can_use_featurizer(self): method test_can_use_statsmodel_inference (line 1029) | def test_can_use_statsmodel_inference(self): method test_ignores_final_intercept (line 1075) | def test_ignores_final_intercept(self): method test_sparse (line 1108) | def test_sparse(self): method test_linear_sparse (line 1118) | def test_linear_sparse(self): method _generate_recoverable_errors (line 1168) | def _generate_recoverable_errors(a_X, X, a_W=None, W=None, featurizer=... method _test_sparse (line 1194) | def _test_sparse(n_p, d_w, n_r): method _test_nuisance_scores (line 1234) | def _test_nuisance_scores(self, use_ray=False): method test_nuisance_scores_with_ray (line 1248) | def test_nuisance_scores_with_ray(self): method test_nuisance_scores_without_ray (line 1255) | def test_nuisance_scores_without_ray(self): method test_compare_nuisance_with_ray_vs_without_ray (line 1259) | def test_compare_nuisance_with_ray_vs_without_ray(self): method test_categories (line 1282) | def test_categories(self): method test_groups (line 1327) | def test_groups(self): method test_treatment_names (line 1350) | def test_treatment_names(self): method test_causal_forest_tune_with_discrete_outcome_and_treatment (line 1385) | def test_causal_forest_tune_with_discrete_outcome_and_treatment(self): FILE: econml/tests/test_dmliv.py class TestDMLIV (line 20) | class TestDMLIV(unittest.TestCase): method test_cate_api (line 21) | def test_cate_api(self): method test_accuracy (line 159) | def test_accuracy(self): method test_groups (line 205) | def test_groups(self): FILE: econml/tests/test_dominicks.py function test_dominicks (line 17) | def test_dominicks(): FILE: econml/tests/test_dowhy.py class TestDowhy (line 17) | class TestDowhy(unittest.TestCase): method _get_data (line 19) | def _get_data(self): method test_dowhy (line 26) | def test_dowhy(self): method test_store_dataframe_name (line 83) | def test_store_dataframe_name(self): method test_dowhy_without_fit (line 99) | def test_dowhy_without_fit(self): FILE: econml/tests/test_driv.py class TestDRIV (line 27) | class TestDRIV(unittest.TestCase): method _test_cate_api (line 28) | def _test_cate_api(self, use_ray=False): method test_cate_api_with_ray (line 226) | def test_cate_api_with_ray(self): method test_cate_api_without_ray (line 233) | def test_cate_api_without_ray(self): method _test_accuracy (line 236) | def _test_accuracy(self, use_ray=False): method test_accuracy_with_ray (line 299) | def test_accuracy_with_ray(self): method test_accuracy_without_ray (line 306) | def test_accuracy_without_ray(self): method test_fit_cov_directly (line 309) | def test_fit_cov_directly(self): method test_groups (line 346) | def test_groups(self): FILE: econml/tests/test_drlearner.py class TestDRLearner (line 34) | class TestDRLearner(unittest.TestCase): method setUpClass (line 37) | def setUpClass(cls): method _test_cate_api (line 63) | def _test_cate_api(self, use_ray=False): method test_test_cate_api_with_ray (line 307) | def test_test_cate_api_with_ray(self): method test_test_cate_api_without_ray (line 314) | def test_test_cate_api_without_ray(self): method test_can_use_vectors (line 318) | def test_can_use_vectors(self): method test_can_use_sample_weights (line 330) | def test_can_use_sample_weights(self): method test_discrete_treatments (line 341) | def test_discrete_treatments(self): method test_can_custom_splitter (line 365) | def test_can_custom_splitter(self): method test_can_use_statsmodel_inference (line 387) | def test_can_use_statsmodel_inference(self): method test_drlearner_all_attributes (line 431) | def test_drlearner_all_attributes(self): method _test_drlearner_with_inference_all_attributes (line 554) | def _test_drlearner_with_inference_all_attributes(self, use_ray): method test_drlearner_with_inference_all_attributes_with_ray (line 764) | def test_drlearner_with_inference_all_attributes_with_ray(self): method test_drlearner_with_inference_all_attributes_without_ray (line 771) | def test_drlearner_with_inference_all_attributes_without_ray(self): method _check_with_interval (line 775) | def _check_with_interval(truth, point, lower, upper): method test_DRLearner (line 780) | def test_DRLearner(self): method test_sparse (line 798) | def test_sparse(self): method test_groups (line 842) | def test_groups(self): method test_score (line 867) | def test_score(self): method test_multitask_model_final (line 898) | def test_multitask_model_final(self): method _test_te (line 916) | def _test_te(self, learner_instance, tol, te_type="const"): method _test_with_W (line 934) | def _test_with_W(self, learner_instance, tol): method _test_inputs (line 950) | def _test_inputs(self, learner_instance): method _untreated_outcome (line 969) | def _untreated_outcome(cls, x): method _const_te (line 973) | def _const_te(cls, x): method _heterogeneous_te (line 977) | def _heterogeneous_te(cls, x): method _generate_data (line 981) | def _generate_data(cls, n, d, untreated_outcome, treatment_effect, pro... class TestSampleTrimming (line 1005) | class TestSampleTrimming(unittest.TestCase): method setUpClass (line 1009) | def setUpClass(cls): method test_trimming_disabled_by_default (line 1022) | def test_trimming_disabled_by_default(self): method test_trimming_with_fixed_threshold (line 1034) | def test_trimming_with_fixed_threshold(self): method test_trimming_with_auto_threshold (line 1048) | def test_trimming_with_auto_threshold(self): method test_higher_threshold_trims_more (line 1062) | def test_higher_threshold_trims_more(self): method test_trimming_uses_raw_propensities (line 1082) | def test_trimming_uses_raw_propensities(self): method test_trimming_warning_when_threshold_less_than_min_propensity (line 1107) | def test_trimming_warning_when_threshold_less_than_min_propensity(self): method test_no_warning_when_threshold_greater_than_min_propensity (line 1130) | def test_no_warning_when_threshold_greater_than_min_propensity(self): method test_trimming_with_linear_drlearner (line 1153) | def test_trimming_with_linear_drlearner(self): method test_trimming_with_sparse_linear_drlearner (line 1170) | def test_trimming_with_sparse_linear_drlearner(self): method test_trimming_with_forest_drlearner (line 1183) | def test_trimming_with_forest_drlearner(self): method test_score_respects_trimming (line 1197) | def test_score_respects_trimming(self): method test_trimming_with_sample_weights (line 1211) | def test_trimming_with_sample_weights(self): method test_trimming_preserves_effect_estimation (line 1227) | def test_trimming_preserves_effect_estimation(self): method test_multitreatment_trimming_warns (line 1250) | def test_multitreatment_trimming_warns(self): method test_auto_trimming_uniform_propensity_full_range (line 1276) | def test_auto_trimming_uniform_propensity_full_range(self): method test_auto_trimming_uniform_propensity_narrow_range (line 1316) | def test_auto_trimming_uniform_propensity_narrow_range(self): FILE: econml/tests/test_drtester.py class TestDRTester (line 12) | class TestDRTester(unittest.TestCase): method _get_data (line 15) | def _get_data(num_treatments=1): method test_multi (line 58) | def test_multi(self): method test_binary (line 111) | def test_binary(self): method test_nuisance_val_fit (line 159) | def test_nuisance_val_fit(self): method test_exceptions (line 200) | def test_exceptions(self): FILE: econml/tests/test_dynamic_dml.py class TestDynamicDML (line 19) | class TestDynamicDML(unittest.TestCase): method test_cate_api (line 21) | def test_cate_api(self): method test_perf (line 254) | def test_perf(self): FILE: econml/tests/test_federated_learning.py class FunctionRegressor (line 13) | class FunctionRegressor: method __init__ (line 16) | def __init__(self, func): method fit (line 19) | def fit(self, X, y, sample_weight=None): method predict (line 22) | def predict(self, X): class FunctionClassifier (line 26) | class FunctionClassifier(FunctionRegressor): method __init__ (line 29) | def __init__(self, func): method predict_proba (line 32) | def predict_proba(self, X): class TestFederatedLearning (line 36) | class TestFederatedLearning(unittest.TestCase): method test_lineardrlearner (line 52) | def test_lineardrlearner(self): method test_splitting_works (line 132) | def test_splitting_works(self): FILE: econml/tests/test_grf_cython.py class TestGRFCython (line 12) | class TestGRFCython(unittest.TestCase): method _get_base_config (line 14) | def _get_base_config(self, n_features=2, n_t=2, n_samples_train=1000): method _get_base_honest_config (line 40) | def _get_base_honest_config(self, n_features=2, n_t=2, n_samples_train... method _get_cython_objects (line 66) | def _get_cython_objects(self, *, criterion, n_features, n_y, n_outputs... method _get_continuous_data (line 91) | def _get_continuous_data(self, config): method _train_tree (line 104) | def _train_tree(self, config, X, y): method _get_true_quantities (line 112) | def _get_true_quantities(self, config, X, y, mask, criterion): method _get_node_quantities (line 129) | def _get_node_quantities(self, tree, node_id): method _test_tree_quantities (line 133) | def _test_tree_quantities(self, base_config_gen, criterion): method test_dishonest_tree (line 197) | def test_dishonest_tree(self): method test_honest_tree (line 201) | def test_honest_tree(self): method test_honest_dishonest_equivalency (line 205) | def test_honest_dishonest_equivalency(self): method test_min_var_leaf (line 234) | def test_min_var_leaf(self): method test_fast_eigv (line 256) | def test_fast_eigv(self): method test_linalg (line 269) | def test_linalg(self): FILE: econml/tests/test_grf_python.py class TestGRFPython (line 15) | class TestGRFPython(unittest.TestCase): method _get_base_config (line 17) | def _get_base_config(self): method _get_regression_data (line 23) | def _get_regression_data(self, n, n_features, random_state): method test_regression_tree_internals (line 32) | def test_regression_tree_internals(self): method _get_causal_data (line 161) | def _get_causal_data(self, n, n_features, n_treatments, random_state): method _get_true_quantities (line 171) | def _get_true_quantities(self, X, T, y, mask, criterion, fit_intercept... method _get_node_quantities (line 207) | def _get_node_quantities(self, tree, node_id): method _train_causal_forest (line 211) | def _train_causal_forest(self, X, T, y, config, sample_weight=None): method _train_iv_forest (line 214) | def _train_iv_forest(self, X, T, y, config, sample_weight=None): method _test_causal_tree_internals (line 217) | def _test_causal_tree_internals(self, trainer): method _test_causal_honesty (line 243) | def _test_causal_honesty(self, trainer): method test_causal_tree (line 312) | def test_causal_tree(self,): method test_iv_tree (line 316) | def test_iv_tree(self,): method test_min_var_leaf (line 320) | def test_min_var_leaf(self,): method test_subsampling (line 358) | def test_subsampling(self,): method _get_step_regression_data (line 406) | def _get_step_regression_data(self, n, n_features, random_state): method test_var (line 412) | def test_var(self,): method test_projection (line 471) | def test_projection(self,): method test_feature_importances (line 503) | def test_feature_importances(self,): method test_non_standard_input (line 571) | def test_non_standard_input(self,): method test_raise_exceptions (line 590) | def test_raise_exceptions(self,): method test_warm_start (line 630) | def test_warm_start(self,): method test_multioutput (line 656) | def test_multioutput(self,): method test_pickling (line 693) | def test_pickling(self,): FILE: econml/tests/test_inference.py class TestInference (line 20) | class TestInference(unittest.TestCase): method setUpClass (line 23) | def setUpClass(cls): method test_summary (line 35) | def test_summary(self): method test_summary_discrete (line 131) | def test_summary_discrete(self): method test_degenerate_cases (line 232) | def test_degenerate_cases(self): method test_can_summarize (line 278) | def test_can_summarize(self): method test_alpha (line 295) | def test_alpha(self): method test_inference_with_none_stderr (line 307) | def test_inference_with_none_stderr(self): method test_auto_inference (line 343) | def test_auto_inference(self): method test_pickle_inferenceresult (line 392) | def test_pickle_inferenceresult(self): method test_mean_pred_stderr (line 403) | def test_mean_pred_stderr(self): method test_isolate_inferenceresult_from_estimator (line 424) | def test_isolate_inferenceresult_from_estimator(self): method test_translate (line 433) | def test_translate(self): method test_scale (line 446) | def test_scale(self): class _NoFeatNamesEst (line 459) | class _NoFeatNamesEst: method __init__ (line 460) | def __init__(self, cate_est): method __getattr__ (line 463) | def __getattr__(self, name): FILE: econml/tests/test_integration.py class TestPandasIntegration (line 22) | class TestPandasIntegration(unittest.TestCase): method setUpClass (line 25) | def setUpClass(cls): method test_dml (line 54) | def test_dml(self): method test_orf (line 120) | def test_orf(self): method test_metalearners (line 141) | def test_metalearners(self): method test_drlearners (line 162) | def test_drlearners(self): method test_orthoiv (line 191) | def test_orthoiv(self): method test_cat_treatments (line 206) | def test_cat_treatments(self): method _check_input_names (line 229) | def _check_input_names(self, summary_table, method _check_popsum_names (line 258) | def _check_popsum_names(self, popsum, Y_multi=False): FILE: econml/tests/test_linear_model.py class TestLassoExtensions (line 16) | class TestLassoExtensions(unittest.TestCase): method setUpClass (line 20) | def setUpClass(cls): method test_can_clone (line 51) | def test_can_clone(self): method test_one_DGP (line 59) | def test_one_DGP(self): method test_mixed_DGP (line 99) | def test_mixed_DGP(self): method test_multiple_outputs (line 118) | def test_multiple_outputs(self): method test_no_weights_cv (line 131) | def test_no_weights_cv(self): method test_weighted_KFold (line 150) | def test_weighted_KFold(self): method test_balanced_weights_cv (line 165) | def test_balanced_weights_cv(self): method test_multiple_outputs_no_weights_cv (line 214) | def test_multiple_outputs_no_weights_cv(self): method test_multiple_outputs_balanced_weights_cv (line 234) | def test_multiple_outputs_balanced_weights_cv(self): method test_wrapper_attributes (line 276) | def test_wrapper_attributes(self): method test_debiased_lasso_one_DGP (line 303) | def test_debiased_lasso_one_DGP(self): method test_debiased_lasso_mixed_DGP (line 340) | def test_debiased_lasso_mixed_DGP(self): method test_multi_output_debiased_lasso (line 352) | def test_multi_output_debiased_lasso(self): method _check_debiased_CI (line 404) | def _check_debiased_CI(self, method _check_debiased_CI_2D (line 425) | def _check_debiased_CI_2D(self, method _check_debiased_coefs (line 447) | def _check_debiased_coefs(self, X, y, sample_weight, expected_coefs, e... method _compare_with_lasso (line 464) | def _compare_with_lasso(self, lasso_X, lasso_y, wlasso_X, wlasso_y, sa... method _compare_with_lasso_cv (line 482) | def _compare_with_lasso_cv(self, lasso_X, lasso_y, wlasso_X, wlasso_y, method _map_splitter (line 507) | def _map_splitter(self, weighted_splits, n_expanded, index_mapper): class TestSelectiveRegularization (line 520) | class TestSelectiveRegularization(unittest.TestCase): method test_against_ridge_ground_truth (line 524) | def test_against_ridge_ground_truth(self): method test_intercept (line 545) | def test_intercept(self): method test_vectors_and_arrays (line 567) | def test_vectors_and_arrays(self): method test_can_use_sample_weights (line 576) | def test_can_use_sample_weights(self): method test_can_slice (line 596) | def test_can_slice(self): method test_can_use_index_lambda (line 613) | def test_can_use_index_lambda(self): method test_can_pass_through_attributes (line 631) | def test_can_pass_through_attributes(self): method test_can_clone_selective_regularization (line 654) | def test_can_clone_selective_regularization(self): FILE: econml/tests/test_metalearners.py class TestMetalearners (line 13) | class TestMetalearners(unittest.TestCase): method setUpClass (line 16) | def setUpClass(cls): method test_TLearner (line 47) | def test_TLearner(self): method test_SLearner (line 59) | def test_SLearner(self): method test_XLearner (line 77) | def test_XLearner(self): method test_DALearner (line 88) | def test_DALearner(self): method _test_te (line 100) | def _test_te(self, learner_instance, T0, T1, tol, te_type="const", mul... method _test_inputs (line 133) | def _test_inputs(self, learner_instance, T0, T1): method _const_te (line 147) | def _const_te(cls, x): method _heterogeneous_te (line 151) | def _heterogeneous_te(cls, x): method _generate_data (line 155) | def _generate_data(cls, n, d, beta, treatment_effect, multi_y): FILE: econml/tests/test_missing_values.py class ModelNuisance (line 24) | class ModelNuisance: method __init__ (line 25) | def __init__(self, model_t, model_y): method train (line 29) | def train(self, is_selecting, folds, Y, T, W=None): method predict (line 34) | def predict(self, Y, T, W=None): class ModelFinal (line 38) | class ModelFinal: method __init__ (line 40) | def __init__(self): method fit (line 43) | def fit(self, Y, T, W=None, nuisances=None): method predict (line 48) | def predict(self): method score (line 52) | def score(self, Y, T, W=None, nuisances=None): class ParametricModelFinalForMissing (line 57) | class ParametricModelFinalForMissing: method __init__ (line 58) | def __init__(self, model_final): method fit (line 61) | def fit(self, *args, **kwargs): method predict (line 70) | def predict(self, *args, **kwargs): method prediction_stderr (line 74) | def prediction_stderr(self, *args, **kwargs): class NonParamModelFinal (line 78) | class NonParamModelFinal: method __init__ (line 79) | def __init__(self, pipeline): method fit (line 82) | def fit(self, *args, **kwargs): method predict (line 88) | def predict(self, *args, **kwargs): method predict_proba (line 91) | def predict_proba(self, *args, **kwargs): function create_data_dict (line 95) | def create_data_dict(Y, T, X, X_missing, W, W_missing, Z, X_has_missing=... class OrthoLearner (line 103) | class OrthoLearner(_OrthoLearner): method _gen_ortho_learner_model_nuisance (line 104) | def _gen_ortho_learner_model_nuisance(self): method _gen_ortho_learner_model_final (line 110) | def _gen_ortho_learner_model_final(self): class TestMissing (line 114) | class TestMissing(unittest.TestCase): method test_missing (line 116) | def test_missing(self): method test_missing2 (line 143) | def test_missing2(self): FILE: econml/tests/test_model_selection.py class TestModelSelection (line 19) | class TestModelSelection(unittest.TestCase): method _simple_dgp (line 21) | def _simple_dgp(self, n, d_x, d_w, discrete_treatment): method test_poly (line 37) | def test_poly(self): method test_all_strings (line 76) | def test_all_strings(self): method test_list_selection (line 94) | def test_list_selection(self): method test_sklearn_model_selection (line 120) | def test_sklearn_model_selection(self): method test_fixed_model_scoring (line 139) | def test_fixed_model_scoring(self): FILE: econml/tests/test_montecarlo.py class TestMonteCarlo (line 14) | class TestMonteCarlo(unittest.TestCase): method test_montecarlo (line 16) | def test_montecarlo(self): method test_discrete_treatment (line 32) | def test_discrete_treatment(self): method test_parameter_passing (line 51) | def test_parameter_passing(self): FILE: econml/tests/test_notebooks.py function test_notebook (line 28) | def test_notebook(file): FILE: econml/tests/test_orf.py class TestOrthoForest (line 14) | class TestOrthoForest(unittest.TestCase): method setUpClass (line 17) | def setUpClass(cls): method test_continuous_treatments (line 40) | def test_continuous_treatments(self): method test_binary_treatments (line 85) | def test_binary_treatments(self): method test_multiple_treatments (line 138) | def test_multiple_treatments(self): method test_effect_shape (line 164) | def test_effect_shape(self): method test_nuisance_model_has_weights (line 284) | def test_nuisance_model_has_weights(self): method _test_te (line 318) | def _test_te(self, learner_instance, expected_te, tol, treatment_type=... method _test_ci (line 334) | def _test_ci(self, learner_instance, expected_te, tol, treatment_type=... method _const_te (line 359) | def _const_te(cls, x): method _exp_te (line 363) | def _exp_te(cls, x): FILE: econml/tests/test_ortho_learner.py class TestOrthoLearner (line 20) | class TestOrthoLearner(unittest.TestCase): method _test_crossfit (line 22) | def _test_crossfit(self, use_ray): method test_crossfit_with_ray (line 158) | def test_crossfit_with_ray(self): method test_crossfit_without_ray (line 165) | def test_crossfit_without_ray(self): method test_crossfit_comparison (line 169) | def test_crossfit_comparison(self): method _test_ol (line 212) | def _test_ol(self, use_ray): method test_ol_with_ray (line 321) | def test_ol_with_ray(self): method test_ol_without_ray (line 324) | def test_ol_without_ray(self): method test_ol_no_score_final (line 327) | def test_ol_no_score_final(self): method test_ol_nuisance_scores (line 375) | def test_ol_nuisance_scores(self): method test_ol_discrete_treatment (line 432) | def test_ol_discrete_treatment(self): FILE: econml/tests/test_policy_forest.py class TestPolicyForest (line 26) | class TestPolicyForest(unittest.TestCase): method _get_base_config (line 28) | def _get_base_config(self): method _get_policy_data (line 34) | def _get_policy_data(self, n, n_features, random_state, n_outcomes=2): method _get_true_quantities (line 43) | def _get_true_quantities(self, X, y, mask, sample_weight=None): method _get_node_quantities (line 51) | def _get_node_quantities(self, tree, node_id): method _train_policy_forest (line 54) | def _train_policy_forest(self, X, y, config, sample_weight=None): method _train_dr_policy_forest (line 57) | def _train_dr_policy_forest(self, X, y, config, sample_weight=None): method _train_dr_policy_tree (line 75) | def _train_dr_policy_tree(self, X, y, config, sample_weight=None): method _test_policy_tree_internals (line 95) | def _test_policy_tree_internals(self, trainer): method _test_policy_honesty (line 113) | def _test_policy_honesty(self, trainer, dr=False): method test_policy_tree (line 159) | def test_policy_tree(self,): method test_drpolicy_tree (line 163) | def test_drpolicy_tree(self,): method test_drpolicy_forest (line 166) | def test_drpolicy_forest(self,): method test_subsampling (line 170) | def test_subsampling(self,): method test_feature_importances (line 215) | def test_feature_importances(self,): method test_non_standard_input (line 278) | def test_non_standard_input(self,): method test_raise_exceptions (line 337) | def test_raise_exceptions(self,): method test_warm_start (line 364) | def test_warm_start(self,): method test_plotting (line 393) | def test_plotting(self): method test_pickling (line 428) | def test_pickling(self,): FILE: econml/tests/test_random_state.py class TestRandomState (line 13) | class TestRandomState(unittest.TestCase): method _make_data (line 16) | def _make_data(n, p): method _test_random_state (line 37) | def _test_random_state(est, X_test, Y, T, **kwargs): method test_dml_random_state (line 47) | def test_dml_random_state(self): method test_dr_random_state (line 66) | def test_dr_random_state(self): method test_orthoiv_random_state (line 81) | def test_orthoiv_random_state(self): FILE: econml/tests/test_refit.py class TestRefit (line 17) | class TestRefit(unittest.TestCase): method _get_data (line 19) | def _get_data(self): method test_dml (line 26) | def test_dml(self): method test_nonparam_dml (line 109) | def test_nonparam_dml(self): method test_drlearner (line 141) | def test_drlearner(self): method test_orthoiv (line 172) | def test_orthoiv(self): method test_can_set_discrete_treatment (line 249) | def test_can_set_discrete_treatment(self): method test_refit_final_inference (line 264) | def test_refit_final_inference(self): method test_rlearner_residuals (line 280) | def test_rlearner_residuals(self): FILE: econml/tests/test_rscorer.py function _fit_model (line 17) | def _fit_model(name, model, Y, T, X): class TestRScorer (line 21) | class TestRScorer(unittest.TestCase): method _get_data (line 23) | def _get_data(self, discrete_outcome=False): method test_comparison (line 41) | def test_comparison(self): FILE: econml/tests/test_sensitivity_analysis.py class TestSensitivityAnalysis (line 15) | class TestSensitivityAnalysis(unittest.TestCase): method test_params (line 17) | def test_params(self): method test_invalid_params (line 101) | def test_invalid_params(self): FILE: econml/tests/test_shap.py class TestShap (line 16) | class TestShap(unittest.TestCase): method test_continuous_t (line 17) | def test_continuous_t(self): method test_discrete_t (line 66) | def test_discrete_t(self): method test_identical_output (line 126) | def test_identical_output(self): FILE: econml/tests/test_statsmodels.py class StatsModelsOLS (line 28) | class StatsModelsOLS: method __init__ (line 46) | def __init__(self, fit_intercept=True, fit_args={}): method fit (line 50) | def fit(self, X, y, sample_weight=None): method predict (line 78) | def predict(self, X): method predict_interval (line 96) | def predict_interval(self, X, alpha=.05): method coef_ (line 121) | def coef_(self): method coef__interval (line 127) | def coef__interval(self, alpha): method intercept_ (line 134) | def intercept_(self): method intercept__interval (line 140) | def intercept__interval(self, alpha): function _compare_classes (line 147) | def _compare_classes(est, lr, X_test, alpha=.05, tol=1e-12): function _summarize (line 163) | def _summarize(X, y, w=None): function _compare_dml_classes (line 244) | def _compare_dml_classes(est, lr, X_test, alpha=.05, tol=1e-10): function _compare_dr_classes (line 256) | def _compare_dr_classes(est, lr, X_test, alpha=.05, tol=1e-10): class TestStatsModels (line 269) | class TestStatsModels(unittest.TestCase): method test_comp_with_lr (line 271) | def test_comp_with_lr(self): method test_o_dtype (line 330) | def test_o_dtype(self): method test_inference (line 349) | def test_inference(self): method test_comp_with_statsmodels (line 453) | def test_comp_with_statsmodels(self): method test_sum_vs_original (line 580) | def test_sum_vs_original(self): method test_dml_sum_vs_original (line 676) | def test_dml_sum_vs_original(self): method test_nonparamdml_sum_vs_original (line 760) | def test_nonparamdml_sum_vs_original(self): method test_dr_sum_vs_original (line 843) | def test_dr_sum_vs_original(self): method test_lineardriv_sum_vs_original (line 927) | def test_lineardriv_sum_vs_original(self): method test_dmliv_sum_vs_original (line 1023) | def test_dmliv_sum_vs_original(self): method test_dml_multi_dim_treatment_outcome (line 1117) | def test_dml_multi_dim_treatment_outcome(self): class TestStatsModels2SLS (line 1245) | class TestStatsModels2SLS(unittest.TestCase): method test_comp_with_IV2SLS (line 1246) | def test_comp_with_IV2SLS(self): FILE: econml/tests/test_treatment_featurization.py class DGP (line 22) | class DGP(): method __init__ (line 23) | def __init__(self, method gen_Y (line 59) | def gen_Y(self): method gen_X (line 64) | def gen_X(self): method gen_T (line 68) | def gen_T(self): method gen_Z (line 74) | def gen_Z(self): method gen_data (line 84) | def gen_data(self): function actual_effect (line 107) | def actual_effect(y_of_t, T0, T1): function nuisance_T (line 111) | def nuisance_T(X): function nuisance_Y (line 115) | def nuisance_Y(X): function identity_y_of_t (line 120) | def identity_y_of_t(T): function identity_actual_marginal (line 124) | def identity_actual_marginal(T): function identity_actual_cme (line 128) | def identity_actual_cme(): function poly_y_of_t (line 136) | def poly_y_of_t(T): function poly_actual_marginal (line 140) | def poly_actual_marginal(t): function poly_actual_cme (line 144) | def poly_actual_cme(): function poly_func_transform (line 148) | def poly_func_transform(x): function poly_1d_actual_cme (line 157) | def poly_1d_actual_cme(): function poly_1d_func_transform (line 161) | def poly_1d_func_transform(x): function sum_y_of_t (line 170) | def sum_y_of_t(T): function sum_actual_cme (line 174) | def sum_actual_cme(): function sum_actual_marginal (line 178) | def sum_actual_marginal(t): function sum_func_transform (line 182) | def sum_func_transform(x): function sum_squeeze_func_transform (line 190) | def sum_squeeze_func_transform(x): class TestTreatmentFeaturization (line 198) | class TestTreatmentFeaturization(unittest.TestCase): method test_featurization (line 200) | def test_featurization(self): method test_jac (line 486) | def test_jac(self): method test_fail_discrete_treatment_and_treatment_featurizer (line 513) | def test_fail_discrete_treatment_and_treatment_featurizer(self): method test_cate_treatment_names_edge_cases (line 553) | def test_cate_treatment_names_edge_cases(self): method test_alpha_passthrough (line 577) | def test_alpha_passthrough(self): method test_identity_feat_with_cate_api (line 598) | def test_identity_feat_with_cate_api(self): FILE: econml/tests/test_tree.py class TestTree (line 13) | class TestTree(unittest.TestCase): method _get_base_config (line 15) | def _get_base_config(self): method _get_base_honest_config (line 42) | def _get_base_honest_config(self): method _get_cython_objects (line 69) | def _get_cython_objects(self, *, n_features, n_y, n_outputs, n_relevan... method _get_continuous_data (line 88) | def _get_continuous_data(self, config): method _get_binary_data (line 99) | def _get_binary_data(self, config): method _train_tree (line 110) | def _train_tree(self, config, X, y): method _test_tree_continuous (line 118) | def _test_tree_continuous(self, base_config_gen): method test_dishonest_tree (line 238) | def test_dishonest_tree(self): method test_honest_tree (line 241) | def test_honest_tree(self): method test_multivariable_split (line 244) | def test_multivariable_split(self): method test_honest_values (line 251) | def test_honest_values(self): method test_noisy_instance (line 260) | def test_noisy_instance(self): FILE: econml/tests/test_two_stage_least_squares.py class Test2SLS (line 16) | class Test2SLS(unittest.TestCase): method test_hermite_shape (line 18) | def test_hermite_shape(self): method test_hermite_results (line 27) | def test_hermite_results(self): method test_hermite_approx (line 42) | def test_hermite_approx(self): method test_2sls_shape (line 60) | def test_2sls_shape(self): method test_marg_eff (line 94) | def test_marg_eff(self): method test_2sls (line 125) | def test_2sls(self): FILE: econml/tests/test_utilities.py class TestUtilities (line 17) | class TestUtilities(unittest.TestCase): method test_check_high_dimensional (line 18) | def test_check_high_dimensional(self): method test_cross_product (line 28) | def test_cross_product(self): method test_einsum_errors (line 46) | def test_einsum_errors(self): method test_einsum_basic (line 77) | def test_einsum_basic(self): method test_transpose_compatible (line 95) | def test_transpose_compatible(self): method test_inverse_onehot (line 108) | def test_inverse_onehot(self): method test_einsum_random (line 116) | def test_einsum_random(self): method test_transpose_dictionary (line 139) | def test_transpose_dictionary(self): method test_deprecated (line 145) | def test_deprecated(self): method test_deprecate_positional (line 172) | def test_deprecate_positional(self): method test_single_strata_from_discrete_array (line 191) | def test_single_strata_from_discrete_array(self): FILE: econml/tests/utilities.py class GroupingModel (line 8) | class GroupingModel: method __init__ (line 20) | def __init__(self, model, total, limits, n_copies): method validate (line 26) | def validate(self, y, skip_group_counts=False): method fit (line 40) | def fit(self, X, y): method predict (line 45) | def predict(self, X): class NestedModel (line 49) | class NestedModel(GroupingModel): method cv (line 59) | def cv(self): method cv (line 63) | def cv(self, value): method fit (line 66) | def fit(self, X, y): FILE: econml/tree/_tree_classes.py class BaseTree (line 22) | class BaseTree(BaseEstimator): method __init__ (line 24) | def __init__(self, *, method _get_valid_criteria (line 52) | def _get_valid_criteria(self): method _get_valid_min_var_leaf_criteria (line 55) | def _get_valid_min_var_leaf_criteria(self): method _get_store_jac (line 58) | def _get_store_jac(self): method get_depth (line 61) | def get_depth(self): method get_n_leaves (line 76) | def get_n_leaves(self): method fit (line 87) | def fit(self, X, y, n_y, n_outputs, n_relevant_outputs, sample_weight=... method _validate_X_predict (line 281) | def _validate_X_predict(self, X, check_input): method get_train_test_split_inds (line 295) | def get_train_test_split_inds(self,): method apply (line 313) | def apply(self, X, check_input=True): method decision_path (line 337) | def decision_path(self, X, check_input=True): method n_features_ (line 361) | def n_features_(self): FILE: econml/utilities.py class IdentityFeatures (line 31) | class IdentityFeatures(TransformerMixin): method fit (line 34) | def fit(self, X): method transform (line 38) | def transform(self, X): function parse_final_model_params (line 43) | def parse_final_model_params(coef, intercept, d_y, d_t, d_t_in, bias_par... function check_high_dimensional (line 61) | def check_high_dimensional(X, T, *, threshold, featurizer=None, discrete... function inverse_onehot (line 78) | def inverse_onehot(T): function issparse (line 99) | def issparse(X): function iscoo (line 119) | def iscoo(X): function tocoo (line 136) | def tocoo(X): function todense (line 152) | def todense(X): function size (line 168) | def size(X): function shape (line 184) | def shape(X): function ndim (line 189) | def ndim(X): function reshape (line 194) | def reshape(X, shape): function _apply (line 222) | def _apply(op, *XS): function tensordot (line 240) | def tensordot(X1, X2, axes): function cross_product (line 262) | def cross_product(*XS): function stack (line 296) | def stack(XS, axis=0): function concatenate (line 322) | def concatenate(XS, axis=0): function hstack (line 346) | def hstack(XS): function vstack (line 367) | def vstack(XS): function transpose (line 389) | def transpose(X, axes=None): function add_intercept (line 414) | def add_intercept(X): function reshape_Y_T (line 431) | def reshape_Y_T(Y, T): function _get_ensure_finite_arg (line 461) | def _get_ensure_finite_arg(ensure_all_finite: Union[str, bool]) -> dict[... function check_inputs (line 469) | def check_inputs(Y, T, X, W=None, multi_output_T=True, multi_output_Y=True, function check_input_arrays (line 546) | def check_input_arrays(*args, validate_len=True, force_all_finite=True, ... function get_input_columns (line 608) | def get_input_columns(X, prefix="X"): function get_feature_names_or_default (line 652) | def get_feature_names_or_default(featurizer, feature_names, prefix="feat... function check_models (line 705) | def check_models(models, n): function broadcast_unit_treatments (line 736) | def broadcast_unit_treatments(X, d_t): function reshape_treatmentwise_effects (line 761) | def reshape_treatmentwise_effects(A, d_t, d_y): function reshape_outcomewise_effects (line 786) | def reshape_outcomewise_effects(A, d_y): function einsum_sparse (line 809) | def einsum_sparse(subscripts, *arrs): function filter_none_kwargs (line 948) | def filter_none_kwargs(**kwargs): class WeightedModelWrapper (line 968) | class WeightedModelWrapper: method __init__ (line 984) | def __init__(self, model_instance, sample_type="weighted"): method fit (line 993) | def fit(self, X, y, sample_weight=None): method predict (line 1012) | def predict(self, X): method _weighted_inputs (line 1027) | def _weighted_inputs(self, X, y, sample_weight): method _sampled_inputs (line 1035) | def _sampled_inputs(self, X, y, sample_weight): class MultiModelWrapper (line 1043) | class MultiModelWrapper: method __init__ (line 1052) | def __init__(self, model_list=[]): method fit (line 1056) | def fit(self, Xt, y, sample_weight=None): method predict (line 1083) | def predict(self, Xt): function _safe_norm_ppf (line 1102) | def _safe_norm_ppf(q, loc=0, scale=1): class Summary (line 1114) | class Summary: method __init__ (line 1132) | def __init__(self): method __str__ (line 1136) | def __str__(self): method __repr__ (line 1139) | def __repr__(self): method _repr_html_ (line 1142) | def _repr_html_(self): method add_table (line 1146) | def add_table(self, res, header, index, title): method add_extra_txt (line 1150) | def add_extra_txt(self, etext): method as_text (line 1161) | def as_text(self): method as_latex (line 1175) | def as_latex(self): method as_csv (line 1195) | def as_csv(self): method as_html (line 1209) | def as_html(self): class SeparateModel (line 1224) | class SeparateModel: method __init__ (line 1231) | def __init__(self, *models): method fit (line 1234) | def fit(self, XZ, T): method predict (line 1240) | def predict(self, XZ): method coef_ (line 1249) | def coef_(self): function deprecated (line 1253) | def deprecated(message, category=FutureWarning): function _deprecate_positional (line 1288) | def _deprecate_positional(message, bad_args, category=FutureWarning): class MissingModule (line 1319) | class MissingModule: method __init__ (line 1331) | def __init__(self, msg, exn): method __getattr__ (line 1336) | def __getattr__(self, _): method __call__ (line 1340) | def __call__(self, *args, **kwargs): function transpose_dictionary (line 1344) | def transpose_dictionary(d): function reshape_arrays_2dim (line 1368) | def reshape_arrays_2dim(length, *args): class _RegressionWrapper (line 1397) | class _RegressionWrapper: method __init__ (line 1410) | def __init__(self, clf): method fit (line 1413) | def fit(self, X, y, **kwargs): method predict (line 1427) | def predict(self, X): class _TransformerWrapper (line 1438) | class _TransformerWrapper: method __init__ (line 1441) | def __init__(self, featurizer): method fit (line 1444) | def fit(self, X): method transform (line 1447) | def transform(self, X): method fit_transform (line 1450) | def fit_transform(self, X): method get_feature_names_out (line 1453) | def get_feature_names_out(self, feature_names): method jac (line 1456) | def jac(self, X, epsilon=0.001): function jacify_featurizer (line 1515) | def jacify_featurizer(featurizer): function strata_from_discrete_arrays (line 1520) | def strata_from_discrete_arrays(arrs): function one_hot_encoder (line 1546) | def one_hot_encoder(sparse=False, **kwargs): FILE: econml/validate/drtester.py class DRTester (line 17) | class DRTester: method __init__ (line 122) | def __init__( method get_cv_splitter (line 135) | def get_cv_splitter(self, random_state: int = 123): method get_cv_splits (line 157) | def get_cv_splits(self, vars: np.array, T: np.array): method fit_nuisance (line 182) | def fit_nuisance( method fit_nuisance_train (line 253) | def fit_nuisance_train( method fit_nuisance_cv (line 296) | def fit_nuisance_cv( method get_cate_preds (line 336) | def get_cate_preds( method evaluate_cal (line 369) | def evaluate_cal( method evaluate_blp (line 448) | def evaluate_blp( method evaluate_uplift (line 505) | def evaluate_uplift( method evaluate_all (line 593) | def evaluate_all( class DRtester (line 642) | class DRtester(DRTester): FILE: econml/validate/results.py class CalibrationEvaluationResults (line 7) | class CalibrationEvaluationResults: method __init__ (line 24) | def __init__( method summary (line 34) | def summary(self) -> pd.DataFrame: method plot_cal (line 52) | def plot_cal(self, tmt: Any): class BLPEvaluationResults (line 84) | class BLPEvaluationResults: method __init__ (line 103) | def __init__( method summary (line 115) | def summary(self): class UpliftEvaluationResults (line 136) | class UpliftEvaluationResults: method __init__ (line 159) | def __init__( method summary (line 173) | def summary(self): method plot_uplift (line 193) | def plot_uplift(self, tmt: Any, err_type: str = None): class EvaluationResults (line 250) | class EvaluationResults: method __init__ (line 269) | def __init__( method summary (line 281) | def summary(self): method plot_cal (line 305) | def plot_cal(self, tmt: int): method plot_qini (line 320) | def plot_qini(self, tmt: int, err_type: str = None): method plot_toc (line 339) | def plot_toc(self, tmt: int, err_type: str = None): FILE: econml/validate/sensitivity_analysis.py function sensitivity_summary (line 10) | def sensitivity_summary(theta, sigma, nu, cov, null_hypothesis=0, alpha=... function sensitivity_interval (line 38) | def sensitivity_interval(theta, sigma, nu, cov, alpha, c_y, c_t, rho, in... function RV (line 77) | def RV(theta, sigma, nu, cov, alpha, null_hypothesis=0, interval_type='c... function dml_sensitivity_values (line 138) | def dml_sensitivity_values(t_res, y_res): function dr_sensitivity_values (line 168) | def dr_sensitivity_values(Y, T, y_pred, t_pred): FILE: econml/validate/utils.py function calculate_dr_outcomes (line 7) | def calculate_dr_outcomes( function calc_uplift (line 50) | def calc_uplift( FILE: monte_carlo_tests/monte_carlo_honestforest.py function monte_carlo (line 11) | def monte_carlo(): FILE: monte_carlo_tests/monte_carlo_statsmodels.py class GridSearchCVList (line 20) | class GridSearchCVList: method __init__ (line 22) | def __init__(self, estimator_list, param_grid_list, scoring=None, method fit (line 32) | def fit(self, X, y, **fit_params): method predict (line 39) | def predict(self, X): function _coverage_profile (line 43) | def _coverage_profile(est, X_test, alpha, true_coef, true_effect): function _append_coverage (line 78) | def _append_coverage(key, coverage, est, X_test, alpha, true_coef, true_... function _agg_coverage (line 89) | def _agg_coverage(coverage, qs=np.array([.005, .025, .1, .9, .975, .995])): function plot_coverage (line 104) | def plot_coverage(coverage, cov_key, n, n_exp, hetero_coef_list, d_list,... function print_aggregate (line 133) | def print_aggregate(mean_coverage, std_coverage, q_coverage, file_gen=la... function run_all_mc (line 213) | def run_all_mc(first_stage, folder, n_list, n_exp, hetero_coef_list, d_l... function monte_carlo (line 367) | def monte_carlo(first_stage=lambda: LinearRegression(), folder='lr'): function monte_carlo_lasso (line 381) | def monte_carlo_lasso(first_stage=lambda: WeightedLasso(alpha=0.01, function monte_carlo_rf (line 397) | def monte_carlo_rf(first_stage=lambda: RandomForestRegressor(n_estimator... function monte_carlo_gcv (line 412) | def monte_carlo_gcv(folder='gcv'): FILE: prototypes/dml_iv/coverage_experiment.py class StatsModelLinearRegression (line 33) | class StatsModelLinearRegression: method __init__ (line 34) | def __init__(self): method fit (line 36) | def fit(self, X, y): method predict (line 39) | def predict(self, X): method summary (line 41) | def summary(self): method coef_ (line 44) | def coef_(self): method intercept_ (line 47) | def intercept_(self): class SeparateModel (line 54) | class SeparateModel: method __init__ (line 55) | def __init__(self, model0, model1): method fit (line 59) | def fit(self, XZ, T): method predict (line 65) | def predict(self, XZ): method coef_ (line 76) | def coef_(self): function gen_data (line 102) | def gen_data(data_type, n): function dgp_binary (line 117) | def dgp_binary(X, n, true_fn): function exp (line 128) | def exp(n): FILE: prototypes/dml_iv/deep_dml_iv.py class DeepDMLIV (line 10) | class DeepDMLIV(_BaseDMLIV): method __init__ (line 16) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, h, FILE: prototypes/dml_iv/deep_dr_iv.py class _KerasModel (line 9) | class _KerasModel: method __init__ (line 19) | def __init__(self, h, method fit (line 30) | def fit(self, X, Y, sample_weight=None): method predict (line 36) | def predict(self, X): method __deepcopy__ (line 39) | def __deepcopy__(self, memo): class DeepDRIV (line 44) | class DeepDRIV(DRIV): method __init__ (line 49) | def __init__(self, model_Y_X, model_T_X, model_Z_X, class DeepProjectedDRIV (line 96) | class DeepProjectedDRIV(ProjectedDRIV): method __init__ (line 101) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, class DeepIntentToTreatDRIV (line 148) | class DeepIntentToTreatDRIV(IntentToTreatDRIV): method __init__ (line 153) | def __init__(self, model_Y_X, model_T_XZ, FILE: prototypes/dml_iv/dml_ate_iv.py class DMLATEIV (line 16) | class DMLATEIV: method __init__ (line 30) | def __init__(self, model_Y_X, model_T_X, model_Z_X, n_splits=2, method fit (line 50) | def fit(self, y, T, X, Z): method effect (line 99) | def effect(self, X=None): method normal_effect_interval (line 110) | def normal_effect_interval(self, lower=5, upper=95): method std (line 114) | def std(self): method fitted_nuisances (line 118) | def fitted_nuisances(self): class ProjectedDMLATEIV (line 124) | class ProjectedDMLATEIV: method __init__ (line 139) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, n_splits=2, method fit (line 158) | def fit(self, y, T, X, Z): method effect (line 211) | def effect(self, X=None): method normal_effect_interval (line 222) | def normal_effect_interval(self, lower=5, upper=95): method std (line 227) | def std(self): method fitted_nuisances (line 231) | def fitted_nuisances(self): class SimpleATEIV (line 236) | class SimpleATEIV: method __init__ (line 243) | def __init__(self, model_T_XZ, model_final): method fit (line 253) | def fit(self, y, T, X, Z): method effect (line 276) | def effect(self, X, T0=0, T1=1): method coef_ (line 292) | def coef_(self): FILE: prototypes/dml_iv/dml_iv.py class _BaseDMLIV (line 22) | class _BaseDMLIV: method __init__ (line 38) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_effect, method fit (line 65) | def fit(self, y, T, X, Z, store_final=False): method effect (line 129) | def effect(self, X): method coef_ (line 138) | def coef_(self): method intercept_ (line 142) | def intercept_(self): method effect_model (line 146) | def effect_model(self): method fitted_nuisances (line 150) | def fitted_nuisances(self): class DMLIV (line 156) | class DMLIV(_BaseDMLIV): method __init__ (line 169) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_effect, fea... method refit_final (line 228) | def refit_final(self, model_effect, featurizer): method effect_model (line 244) | def effect_model(self): class GenericDMLIV (line 250) | class GenericDMLIV(_BaseDMLIV): method __init__ (line 266) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, model_effect, method refit_final (line 327) | def refit_final(self, model_effect): method effect_model (line 342) | def effect_model(self): FILE: prototypes/dml_iv/dr_iv.py class _BaseDRIV (line 24) | class _BaseDRIV: method __init__ (line 31) | def __init__(self, nuisance_models, method _check_inputs (line 66) | def _check_inputs(self, y, T, X, Z): method _nuisance_estimates (line 71) | def _nuisance_estimates(self, y, T, X, Z): method _get_split_enum (line 77) | def _get_split_enum(self, y, T, X, Z): method fit (line 95) | def fit(self, y, T, X, Z, store_final=False): method refit_final (line 135) | def refit_final(self, model_effect, opt_reweighted=None): method effect (line 156) | def effect(self, X): method effect_model (line 165) | def effect_model(self): method fitted_nuisances (line 169) | def fitted_nuisances(self): method coef_ (line 176) | def coef_(self): method intercept_ (line 182) | def intercept_(self): class DRIV (line 188) | class DRIV(_BaseDRIV): method __init__ (line 193) | def __init__(self, model_Y_X, model_T_X, model_Z_X, method _check_inputs (line 235) | def _check_inputs(self, y, T, X, Z): method _nuisance_estimates (line 249) | def _nuisance_estimates(self, y, T, X, Z): class ProjectedDRIV (line 281) | class ProjectedDRIV(_BaseDRIV): method __init__ (line 288) | def __init__(self, model_Y_X, model_T_X, model_T_XZ, method _check_inputs (line 330) | def _check_inputs(self, y, T, X, Z): method _nuisance_estimates (line 347) | def _nuisance_estimates(self, y, T, X, Z): class _IntentToTreatDRIV (line 399) | class _IntentToTreatDRIV(_BaseDRIV): method __init__ (line 404) | def __init__(self, model_Y_X, model_T_XZ, method _check_inputs (line 422) | def _check_inputs(self, y, T, X, Z): method _nuisance_estimates (line 436) | def _nuisance_estimates(self, y, T, X, Z): class _DummyCATE (line 464) | class _DummyCATE: method __init__ (line 469) | def __init__(self): method fit (line 472) | def fit(self, y, T, X, Z): method effect (line 475) | def effect(self, X): class IntentToTreatDRIV (line 479) | class IntentToTreatDRIV(_IntentToTreatDRIV): method __init__ (line 484) | def __init__(self, model_Y_X, model_T_XZ, FILE: prototypes/dml_iv/utilities.py class StatsModelLinearRegression (line 23) | class StatsModelLinearRegression: method __init__ (line 25) | def __init__(self, fit_intercept=True, cov_type='nonrobust'): method fit (line 30) | def fit(self, X, y, sample_weight=None): method predict (line 39) | def predict(self, X): method summary (line 44) | def summary(self, *args, **kwargs): method coef_ (line 48) | def coef_(self): method intercept_ (line 54) | def intercept_(self): class ConstantModel (line 61) | class ConstantModel: method __init__ (line 63) | def __init__(self): method fit (line 67) | def fit(self, X, y, sample_weight=None): method predict (line 71) | def predict(self, X): method summary (line 74) | def summary(self, *args, **kwargs): method coef_ (line 78) | def coef_(self): method intercept_ (line 82) | def intercept_(self): class SeparateModel (line 85) | class SeparateModel: method __init__ (line 91) | def __init__(self, model0, model1): method fit (line 96) | def fit(self, XZ, T): method predict (line 103) | def predict(self, XZ): method coef_ (line 114) | def coef_(self): class RegWrapper (line 117) | class RegWrapper: method __init__ (line 125) | def __init__(self, clf): method fit (line 133) | def fit(self, X, y): method predict (line 143) | def predict(self, X): method __getattr__ (line 151) | def __getattr__(self, name): method __deepcopy__ (line 156) | def __deepcopy__(self, memo): class SubsetWrapper (line 159) | class SubsetWrapper: method __init__ (line 165) | def __init__(self, model, inds): method fit (line 175) | def fit(self, X, y, **kwargs): method predict (line 185) | def predict(self, X): method __getattr__ (line 193) | def __getattr__(self, name): method __deepcopy__ (line 198) | def __deepcopy__(self, memo): class WeightWrapper (line 201) | class WeightWrapper: method __init__ (line 211) | def __init__(self, model): method fit (line 222) | def fit(self, X, y, sample_weight=None): method predict (line 236) | def predict(self, X): method coef_ (line 245) | def coef_(self): method intercept_ (line 251) | def intercept_(self): method __getattr__ (line 256) | def __getattr__(self, name): method __deepcopy__ (line 261) | def __deepcopy__(self, memo): class SelectiveLasso (line 266) | class SelectiveLasso: method __init__ (line 268) | def __init__(self, inds, lasso_model): method fit (line 276) | def fit(self, X, y): method predict (line 290) | def predict(self, X): method model (line 298) | def model(self): method coef_ (line 302) | def coef_(self): method intercept_ (line 313) | def intercept_(self): class HonestForest (line 317) | class HonestForest(RandomForestRegressor): method __init__ (line 324) | def __init__(self, forest, local_linear=False, alpha=0.1): method fit (line 337) | def fit(self, X, y, sample_weight=None): method predict (line 375) | def predict(self, X): method predict_interval (line 390) | def predict_interval(self, X, lower=2.5, upper=97.5, little_bags=False): method __getattr__ (line 409) | def __getattr__(self, name): method __deepcopy__ (line 414) | def __deepcopy__(self, memo): FILE: prototypes/dml_iv/xgb_utilities.py class XGBWrapper (line 4) | class XGBWrapper: method __init__ (line 6) | def __init__(self, XGBoost, early_stopping_rounds, eval_metric, val_fr... method fit (line 14) | def fit(self, X, y, sample_weight=None): method predict_proba (line 26) | def predict_proba(self, X): method predict (line 29) | def predict(self, X): method __getattr__ (line 32) | def __getattr__(self, name): method __deepcopy__ (line 37) | def __deepcopy__(self, memo): FILE: prototypes/dynamic_dml/coverage_panel.py function exp (line 13) | def exp(exp_id, dgp, n_units, gamma, s_t, sigma_t): function add_vlines (line 45) | def add_vlines(n_periods, n_treatments, hetero_inds=[]): function run_mc (line 75) | def run_mc(n_exps, n_units, n_x, s_x, n_periods, n_treatments, s_t, sigm... FILE: prototypes/dynamic_dml/coverage_panel_hetero.py function exp (line 13) | def exp(exp_id, dgp, n_units, gamma, s_t, sigma_t, hetero_inds, test_pol... function add_vlines (line 56) | def add_vlines(n_periods, n_treatments, hetero_inds): function run_mc (line 97) | def run_mc(n_exps, n_units, n_x, s_x, n_periods, n_treatments, s_t, sigm... FILE: prototypes/dynamic_dml/dynamic_panel_dgp.py class AbstracDynamicPanelDGP (line 6) | class AbstracDynamicPanelDGP: method __init__ (line 8) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 14) | def create_instance(self, *args, **kwargs): method _gen_data_with_policy (line 17) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method static_policy_data (line 20) | def static_policy_data(self, n_units, tau, random_seed=123): method adaptive_policy_data (line 25) | def adaptive_policy_data(self, n_units, policy_gen, random_seed=123): method static_policy_effect (line 28) | def static_policy_effect(self, tau, mc_samples=1000): method adaptive_policy_effect (line 36) | def adaptive_policy_effect(self, policy_gen, mc_samples=1000): class DynamicPanelDGP (line 45) | class DynamicPanelDGP(AbstracDynamicPanelDGP): method __init__ (line 47) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 50) | def create_instance(self, s_x, sigma_x, sigma_y, conf_str, hetero_stre... method hetero_effect_fn (line 106) | def hetero_effect_fn(self, t, x): method _gen_data_with_policy (line 114) | def _gen_data_with_policy(self, n_units, policy_gen, random_seed=123): method observational_data (line 137) | def observational_data(self, n_units, gamma, s_t, sigma_t, random_seed... class LongRangeDynamicPanelDGP (line 156) | class LongRangeDynamicPanelDGP(DynamicPanelDGP): method __init__ (line 158) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 161) | def create_instance(self, s_x, sigma_x, sigma_y, conf_str, hetero_stre... class EndogenousDynamicPanelDGP (line 209) | class EndogenousDynamicPanelDGP(DynamicPanelDGP): method __init__ (line 211) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 214) | def create_instance(self, s_x, sigma_x, sigma_y, conf_str, hetero_stre... class PastTreatmentHeteroDynamicPanelDGP (line 260) | class PastTreatmentHeteroDynamicPanelDGP(DynamicPanelDGP): method __init__ (line 262) | def __init__(self, n_periods, n_treatments, n_x): method create_instance (line 265) | def create_instance(self, s_x, sigma_x, sigma_y, conf_str, hetero_stre... FILE: prototypes/dynamic_dml/hetero_panel_dynamic_dml.py class HeteroDynamicPanelDML (line 10) | class HeteroDynamicPanelDML: method __init__ (line 12) | def __init__(self, model_t=LassoCV(cv=3), method fit_nuisances (line 25) | def fit_nuisances(self, Y, T, X, groups, n_periods): method _fit_cov_matrix (line 50) | def _fit_cov_matrix(self, resT, resY, models): method fit_final (line 85) | def fit_final(self, Y, T, X, groups, resT, resY, n_periods, hetero_inds): method fit (line 111) | def fit(self, Y, T, X, groups, hetero_inds=np.empty(shape=(0,))): method param (line 126) | def param(self): method param_cov (line 130) | def param_cov(self): method param_stderr (line 134) | def param_stderr(self): method param_interval (line 137) | def param_interval(self, alpha=.05): method _policy_effect_var (line 142) | def _policy_effect_var(self, tau): method _policy_effect_stderr (line 145) | def _policy_effect_stderr(self, tau): method policy_effect (line 148) | def policy_effect(self, tau, subX, groups, alpha=0.05): method adaptive_policy_effect (line 167) | def adaptive_policy_effect(self, X, groups, policy_gen, alpha=.05): FILE: prototypes/dynamic_dml/panel_dynamic_dml.py class DynamicPanelDML (line 9) | class DynamicPanelDML: method __init__ (line 11) | def __init__(self, model_t=LassoCV(cv=3), method fit_nuisances (line 21) | def fit_nuisances(self, Y, T, X, groups, n_periods): method _fit_cov_matrix (line 46) | def _fit_cov_matrix(self, resT, resY, models): method fit_final (line 81) | def fit_final(self, Y, T, X, groups, resT, resY, n_periods): method fit (line 99) | def fit(self, Y, T, X, groups): method param (line 112) | def param(self): method param_cov (line 116) | def param_cov(self): method param_stderr (line 120) | def param_stderr(self): method param_interval (line 123) | def param_interval(self, alpha=.05): method policy_effect (line 128) | def policy_effect(self, tau): method policy_effect_var (line 131) | def policy_effect_var(self, tau): method policy_effect_stderr (line 134) | def policy_effect_stderr(self, tau): method policy_effect_interval (line 137) | def policy_effect_interval(self, tau, alpha=0.05): method adaptive_policy_effect (line 145) | def adaptive_policy_effect(self, X, groups, policy_gen, alpha=.05): FILE: prototypes/orthogonal_forests/causal_tree.py class Node (line 11) | class Node: method __init__ (line 13) | def __init__(self, sample_inds, estimate_inds): class CausalTree (line 25) | class CausalTree: method __init__ (line 27) | def __init__(self, W, x, T, Y, model_T, model_Y, min_leaf_size=20, max... method recursive_split (line 44) | def recursive_split(self, node, split_acc): method create_splits (line 132) | def create_splits(self): method estimate_leafs (line 138) | def estimate_leafs(self, node): method estimate (line 150) | def estimate(self): method print_tree_rec (line 153) | def print_tree_rec(self, node): method print_tree (line 162) | def print_tree(self): method find_tree_node (line 165) | def find_tree_node(self, node, value): method find_split (line 173) | def find_split(self, value): FILE: prototypes/orthogonal_forests/comparison_plots.py function has_plot_controls (line 32) | def has_plot_controls(fname, control_combination): function get_file_key (line 38) | def get_file_key(fname): function sort_fnames (line 44) | def sort_fnames(file_names): function get_file_groups (line 56) | def get_file_groups(agg_fnames, plot_controls): function merge_results (line 73) | def merge_results(sf, input_dir, output_dir, split_files_seeds): function get_results (line 87) | def get_results(fname, dir_name): function save_plots (line 91) | def save_plots(fig, fname, lgd=None): function get_r2 (line 113) | def get_r2(df): function get_metrics (line 117) | def get_metrics(dfs): function generic_joint_plots (line 142) | def generic_joint_plots(file_key, dfs, labels, file_name_prefix): function metrics_subfig (line 174) | def metrics_subfig(dfs, ax, metric, c_scheme=0): function metrics_plots (line 240) | def metrics_plots(file_key, dfs, labels, c_scheme, file_name_prefix): function support_plots (line 254) | def support_plots(all_metrics, labels, file_name_prefix): FILE: prototypes/orthogonal_forests/hetero_dml.py function cross_product (line 11) | def cross_product(X1, X2): class HeteroDML (line 28) | class HeteroDML(object): method __init__ (line 30) | def __init__(self, poly_degree=3, method fit (line 38) | def fit(self, W, x, T, Y): method predict (line 63) | def predict(self, x): class ForestHeteroDML (line 68) | class ForestHeteroDML(object): method __init__ (line 70) | def __init__(self): method fit (line 75) | def fit(self, W, x, T, Y): method predict (line 99) | def predict(self, x): FILE: prototypes/orthogonal_forests/monte_carlo.py function piecewise_linear_te (line 21) | def piecewise_linear_te(x): function step_te (line 29) | def step_te(x): function polynomial_te (line 37) | def polynomial_te(x): function doublez_te (line 45) | def doublez_te(x): FILE: prototypes/orthogonal_forests/ortho_forest.py function _build_tree_in_parallel (line 40) | def _build_tree_in_parallel(W, x, T, Y, min_leaf_size, max_splits, resid... class OrthoTree (line 47) | class OrthoTree(object): method __init__ (line 74) | def __init__(self, min_leaf_size=20, max_splits=10, residualizer=dml, method fit (line 83) | def fit(self, W, x, T, Y): method predict (line 109) | def predict(self, x): class BaseOrthoForest (line 127) | class BaseOrthoForest(object): method __init__ (line 158) | def __init__(self, n_trees=10, min_leaf_size=20, max_splits=10, method fit_forest (line 173) | def fit_forest(self, W, x, T, Y): method fit (line 192) | def fit(self, W, x, T, Y): method predict (line 211) | def predict(self, x): method predict_interval (line 224) | def predict_interval(self, x, lower=5, upper=95): class DishonestOrthoForest (line 245) | class DishonestOrthoForest(BaseOrthoForest): method __init__ (line 294) | def __init__(self, n_trees=10, min_leaf_size=20, max_splits=10, method fit (line 315) | def fit(self, W, x, T, Y): method predict (line 337) | def predict(self, x): method predict_with_weights (line 347) | def predict_with_weights(self, x): method _predict (line 358) | def _predict(self, x, weights=False): method _point_predict (line 369) | def _point_predict(self, x_out, weights=False): method _get_weights (line 387) | def _get_weights(self, x_out): method _get_weighted_model (line 406) | def _get_weighted_model(self, model_instance): method _get_weighted_pipeline (line 416) | def _get_weighted_pipeline(self, model_instance): method _fit_weighted_pipeline (line 424) | def _fit_weighted_pipeline(self, model_instance, x, y, weights): class OrthoForest (line 431) | class OrthoForest(DishonestOrthoForest): method __init__ (line 480) | def __init__(self, n_trees=10, min_leaf_size=20, max_splits=10, method fit (line 497) | def fit(self, W, x, T, Y): method _predict (line 528) | def _predict(self, x, weights=False): method _point_predict (line 539) | def _point_predict(self, x_out, weights=False): method _get_weights (line 557) | def _get_weights(self, x_out): class ModelWrapper (line 579) | class ModelWrapper(object): method __init__ (line 595) | def __init__(self, model_instance, sample_type="weighted"): method __getattr__ (line 604) | def __getattr__(self, name): method _fit (line 611) | def _fit(self, func): method _weighted_inputs (line 619) | def _weighted_inputs(self, X, y, sample_weight): method _sampled_inputs (line 624) | def _sampled_inputs(self, X, y, sample_weight): FILE: prototypes/orthogonal_forests/residualizer.py function dml (line 8) | def dml(W, T, Y, model_T=LassoCV(alphas=[0.01, 0.05, 0.1, 0.3, 0.5, 0.9,... function second_order_dml (line 35) | def second_order_dml(W, T, Y, model_T=LassoCV(alphas=[0.01, 0.05, 0.1, 0...