causalml/inference/meta/xlearner.py [156:209]:
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            self.models_tau_c[group].fit(X_filt[w == 0], d_c)
            self.models_tau_t[group].fit(X_filt[w == 1], d_t)

    def predict(
        self, X, treatment=None, y=None, p=None, return_components=False, verbose=True
    ):
        """Predict treatment effects.

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            treatment (np.array or pd.Series, optional): a treatment vector
            y (np.array or pd.Series, optional): an outcome vector
            p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the
                single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of
                float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores.
            return_components (bool, optional): whether to return outcome for treatment and control seperately
            verbose (bool, optional): whether to output progress logs
        Returns:
            (numpy.ndarray): Predictions of treatment effects.
        """
        X, treatment, y = convert_pd_to_np(X, treatment, y)

        if p is None:
            logger.info("Generating propensity score")
            p = dict()
            for group in self.t_groups:
                p_model = self.propensity_model[group]
                p[group] = p_model.predict(X)
        else:
            p = self._format_p(p, self.t_groups)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        dhat_cs = {}
        dhat_ts = {}

        for i, group in enumerate(self.t_groups):
            model_tau_c = self.models_tau_c[group]
            model_tau_t = self.models_tau_t[group]
            dhat_cs[group] = model_tau_c.predict(X)
            dhat_ts[group] = model_tau_t.predict(X)

            _te = (p[group] * dhat_cs[group] + (1 - p[group]) * dhat_ts[group]).reshape(
                -1, 1
            )
            te[:, i] = np.ravel(_te)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                X_filt = X[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
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causalml/inference/meta/xlearner.py [578:632]:
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            self.models_tau_c[group].fit(X_filt[w == 0], d_c)
            self.models_tau_t[group].fit(X_filt[w == 1], d_t)

    def predict(
        self, X, treatment=None, y=None, p=None, return_components=False, verbose=True
    ):
        """Predict treatment effects.

        Args:
            X (np.matrix or np.array or pd.Dataframe): a feature matrix
            treatment (np.array or pd.Series, optional): a treatment vector
            y (np.array or pd.Series, optional): an outcome vector
            p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the
                single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of
                float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores.
            return_components (bool, optional): whether to return outcome for treatment and control seperately
            return_p_score (bool, optional): whether to return propensity score
            verbose (bool, optional): whether to output progress logs
        Returns:
            (numpy.ndarray): Predictions of treatment effects.
        """
        X, treatment, y = convert_pd_to_np(X, treatment, y)

        if p is None:
            logger.info("Generating propensity score")
            p = dict()
            for group in self.t_groups:
                p_model = self.propensity_model[group]
                p[group] = p_model.predict(X)
        else:
            p = self._format_p(p, self.t_groups)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        dhat_cs = {}
        dhat_ts = {}

        for i, group in enumerate(self.t_groups):
            model_tau_c = self.models_tau_c[group]
            model_tau_t = self.models_tau_t[group]
            dhat_cs[group] = model_tau_c.predict(X)
            dhat_ts[group] = model_tau_t.predict(X)

            _te = (p[group] * dhat_cs[group] + (1 - p[group]) * dhat_ts[group]).reshape(
                -1, 1
            )
            te[:, i] = np.ravel(_te)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                X_filt = X[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
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