econml/iv/dml/_dml.py [419:522]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return super().fit(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight, freq_weight=freq_weight, sample_var=sample_var, groups=groups, cache_values=cache_values, inference=inference) def refit_final(self, *, inference='auto'): return super().refit_final(inference=inference) refit_final.__doc__ = _OrthoLearner.refit_final.__doc__ def score(self, Y, T, Z, X=None, W=None, sample_weight=None): """ Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted residual nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calculates the MSE of the final residual Y on residual T regression. If model_final does not have a score method, then it raises an :exc:`.AttributeError` Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: optional(n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample W: optional(n, d_w) matrix or None (Default=None) Controls for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples Returns ------- score: float The MSE of the final CATE model on the new data. """ # Replacing score from _OrthoLearner, to enforce Z to be required and improve the docstring return super().score(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight) @property def featurizer_(self): """ Get the fitted featurizer. Returns ------- featurizer: object of type(`featurizer`) An instance of the fitted featurizer that was used to preprocess X in the final CATE model training. Available only when featurizer is not None and X is not None. """ return self.ortho_learner_model_final_._featurizer @property def original_featurizer(self): # NOTE: important to use the ortho_learner_model_final_ attribute instead of the # attribute so that the trained featurizer will be passed through return self.ortho_learner_model_final_._original_featurizer def cate_feature_names(self, feature_names=None): """ Get the output feature names. Parameters ---------- feature_names: list of strings of length X.shape[1] or None The names of the input features. If None and X is a dataframe, it defaults to the column names from the dataframe. Returns ------- out_feature_names: list of strings or None The names of the output features :math:`\\phi(X)`, i.e. the features with respect to which the final CATE model for each treatment is linear. It is the names of the features that are associated with each entry of the :meth:`coef_` parameter. Available only when the featurizer is not None and has a method: `get_feature_names(feature_names)`. Otherwise None is returned. """ if self._d_x is None: # Handles the corner case when X=None but featurizer might be not None return None if feature_names is None: feature_names = self._input_names["feature_names"] if self.original_featurizer is None: return feature_names return get_feature_names_or_default(self.original_featurizer, feature_names) @property def model_final_(self): # NOTE This is used by the inference methods and is more for internal use to the library return self.ortho_learner_model_final_._model_final @property def model_cate(self): """ Get the fitted final CATE model. Returns ------- model_cate: object of type(model_final) An instance of the model_final object that was fitted after calling fit which corresponds to the constant marginal CATE model. """ return self.ortho_learner_model_final_._model_final - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - econml/iv/dr/_dr.py [399:502]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return super().fit(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight, freq_weight=freq_weight, sample_var=sample_var, groups=groups, cache_values=cache_values, inference=inference) def refit_final(self, *, inference='auto'): return super().refit_final(inference=inference) refit_final.__doc__ = _OrthoLearner.refit_final.__doc__ def score(self, Y, T, Z, X=None, W=None, sample_weight=None): """ Score the fitted CATE model on a new data set. Generates nuisance parameters for the new data set based on the fitted residual nuisance models created at fit time. It uses the mean prediction of the models fitted by the different crossfit folds. Then calculates the MSE of the final residual Y on residual T regression. If model_final does not have a score method, then it raises an :exc:`.AttributeError` Parameters ---------- Y: (n, d_y) matrix or vector of length n Outcomes for each sample T: (n, d_t) matrix or vector of length n Treatments for each sample Z: (n, d_z) matrix Instruments for each sample X: optional(n, d_x) matrix or None (Default=None) Features for each sample W: optional(n, d_w) matrix or None (Default=None) Controls for each sample sample_weight: optional(n,) vector or None (Default=None) Weights for each samples Returns ------- score: float The MSE of the final CATE model on the new data. """ # Replacing score from _OrthoLearner, to enforce Z to be required and improve the docstring return super().score(Y, T, X=X, W=W, Z=Z, sample_weight=sample_weight) @property def featurizer_(self): """ Get the fitted featurizer. Returns ------- featurizer: object of type(`featurizer`) An instance of the fitted featurizer that was used to preprocess X in the final CATE model training. Available only when featurizer is not None and X is not None. """ return self.ortho_learner_model_final_._featurizer @property def original_featurizer(self): # NOTE: important to use the ortho_learner_model_final_ attribute instead of the # attribute so that the trained featurizer will be passed through return self.ortho_learner_model_final_._original_featurizer def cate_feature_names(self, feature_names=None): """ Get the output feature names. Parameters ---------- feature_names: list of strings of length X.shape[1] or None The names of the input features. If None and X is a dataframe, it defaults to the column names from the dataframe. Returns ------- out_feature_names: list of strings or None The names of the output features :math:`\\phi(X)`, i.e. the features with respect to which the final CATE model for each treatment is linear. It is the names of the features that are associated with each entry of the :meth:`coef_` parameter. Available only when the featurizer is not None and has a method: `get_feature_names(feature_names)`. Otherwise None is returned. """ if self._d_x is None: # Handles the corner case when X=None but featurizer might be not None return None if feature_names is None: feature_names = self._input_names["feature_names"] if self.original_featurizer is None: return feature_names return get_feature_names_or_default(self.original_featurizer, feature_names) @property def model_final_(self): # NOTE This is used by the inference methods and is more for internal use to the library return self.ortho_learner_model_final_._model_final @property def model_cate(self): """ Get the fitted final CATE model. Returns ------- model_cate: object of type(model_final) An instance of the model_final object that was fitted after calling fit which corresponds to the constant marginal CATE model. """ return self.ortho_learner_model_final_._model_final - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -