How to use the causalml.inference.meta.explainer.Explainer function in causalml

- auto (calculates importance based on estimator's default implementation of feature importance;
                    estimator must be tree-based)
                    Note: if none provided, it uses lightgbm's LGBMRegressor as estimator, and "gain" as
                    importance type
            - permutation (calculates importance based on mean decrease in accuracy; estimator can be any form)
        Hint: for permutation, downsample data for better performance especially if X.shape[1] is large

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            method (str): auto, permutation
            normalize (bool): normalize by sum of importances if method=gini (defaults to True)
        """
        explainer = Explainer(method=method, control_name=self.control_name,
                              X=X, tau=tau, model_tau=None, r_learners=self.models_tau,
                              features=features, classes=self._classes, normalize=normalize)
        explainer.plot_importance()

- auto (calculates importance based on estimator's default implementation of feature importance;
                    estimator must be tree-based)
                    Note: if none provided, it uses lightgbm's LGBMRegressor as estimator, and "gain" as
                    importance type
            - permutation (calculates importance based on mean decrease in accuracy; estimator can be any form)
        Hint: for permutation, downsample data for better performance especially if X.shape[1] is large

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (np.array): list/array of feature names. If None, an enumerated list will be used.
            method (str): auto, permutation
            normalize (bool): normalize by sum of importances if method=auto (defaults to True)
        """
        explainer = Explainer(method=method, control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, classes=self._classes, normalize=normalize)
        return explainer.get_importance()

def get_shap_values(self, X=None, model_tau_feature=None, tau=None, features=None):
        """
        Builds a model (using X to predict estimated/actual tau), and then calculates shapley values.
        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
        """
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, classes=self._classes)
        return explainer.get_shap_values()

- auto (calculates importance based on estimator's default implementation of feature importance;
                    estimator must be tree-based)
                    Note: if none provided, it uses lightgbm's LGBMRegressor as estimator, and "gain" as
                    importance type
            - permutation (calculates importance based on mean decrease in accuracy; estimator can be any form)
        Hint: for permutation, downsample data for better performance especially if X.shape[1] is large

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            method (str): auto, permutation
            normalize (bool): normalize by sum of importances if method=auto (defaults to True)
        """
        explainer = Explainer(method=method, control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, classes=self._classes, normalize=normalize)
        explainer.plot_importance()

Args:
            treatment_group (str or int): name of treatment group to create dependency plot on
            feature_idx (str or int): feature index / name to create dependency plot on
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed.
            interaction_idx (optional, str or int): feature index / name used in coloring scheme as interaction feature.
                If "auto" then shap.common.approximate_interactions is used to pick what seems to be the
                strongest interaction (note that to find to true strongest interaction you need to compute
                the SHAP interaction values).
        """
        override_checks = False if shap_dict is None else True
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, override_checks=override_checks,
                              classes=self._classes)
        explainer.plot_shap_dependence(treatment_group=treatment_group,
                                       feature_idx=feature_idx,
                                       shap_dict=shap_dict,
                                       interaction_idx=interaction_idx,
                                       **kwargs)

def get_shap_values(self, X=None, model_tau_feature=None, tau=None, features=None):
        """
        Builds a model (using X to predict estimated/actual tau), and then calculates shapley values.
        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
        """
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, classes=self._classes)
        return explainer.get_shap_values()

def get_shap_values(self, X=None, model_tau_feature=None, tau=None, features=None):
        """
        Builds a model (using X to predict estimated/actual tau), and then calculates shapley values.
        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
        """
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, classes=self._classes)
        return explainer.get_shap_values()

"""
        Plots distribution of shapley values.

        If shapley values have been pre-computed, pass it through the shap_dict parameter.
        If shap_dict is not provided, this builds a new model (using X to predict estimated/actual tau),
        and then calculates shapley values.

        Args:
            X (np.matrix, np.array, or pd.Dataframe): a feature matrix. Required if shap_dict is None.
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed.
        """
        override_checks = False if shap_dict is None else True
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, override_checks=override_checks, classes=self._classes)
        explainer.plot_shap_values(shap_dict=shap_dict)

"""
        Plots distribution of shapley values.

        If shapley values have been pre-computed, pass it through the shap_dict parameter.
        If shap_dict is not provided, this builds a new model (using X to predict estimated/actual tau),
        and then calculates shapley values.

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix. Required if shap_dict is None.
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed.
        """
        override_checks = False if shap_dict is None else True
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, override_checks=override_checks, classes=self._classes)
        explainer.plot_shap_values(shap_dict=shap_dict)

Args:
            treatment_group (str or int): name of treatment group to create dependency plot on
            feature_idx (str or int): feature index / name to create dependency plot on
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            tau (np.array): a treatment effect vector (estimated/actual)
            model_tau_feature (sklearn/lightgbm/xgboost model object): an unfitted model object
            features (optional, np.array): list/array of feature names. If None, an enumerated list will be used.
            shap_dict (optional, dict): a dict of shapley value matrices. If None, shap_dict will be computed.
            interaction_idx (optional, str or int): feature index / name used in coloring scheme as interaction feature.
                If "auto" then shap.common.approximate_interactions is used to pick what seems to be the
                strongest interaction (note that to find to true strongest interaction you need to compute
                the SHAP interaction values).
        """
        override_checks = False if shap_dict is None else True
        explainer = Explainer(method='shapley', control_name=self.control_name,
                              X=X, tau=tau, model_tau=model_tau_feature,
                              features=features, override_checks=override_checks,
                              classes=self._classes)
        explainer.plot_shap_dependence(treatment_group=treatment_group,
                                       feature_idx=feature_idx,
                                       shap_dict=shap_dict,
                                       interaction_idx=interaction_idx,
                                       **kwargs)