causalml/inference/iv/drivlearner.py [752:778]:
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        **kwargs,
    ):
        """
        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,
        )
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causalml/inference/iv/drivlearner.py [791:828]:
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        **kwargs,
    ):
        """
        Plots dependency of shapley values for a specified feature, colored by an interaction feature.

        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.

        This plots the value of the feature on the x-axis and the SHAP value of the same feature
        on the y-axis. This shows how the model depends on the given feature, and is like a
        richer extension of the classical partial dependence plots. Vertical dispersion of the
        data points represents interaction effects.

        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,
        )
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