causalml/inference/meta/tlearner.py [112:129]:
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                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info('Error metrics for group {}'.format(group))
                regression_metrics(y_filt, yhat, w)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        for i, group in enumerate(self.t_groups):
            te[:, i] = yhat_ts[group] - yhat_cs[group]

        if not return_components:
            return te
        else:
            return te, yhat_cs, yhat_ts

    def fit_predict(self, X, treatment, y, return_ci=False, n_bootstraps=1000, bootstrap_size=10000,
                    return_components=False, verbose=True):
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causalml/inference/meta/slearner.py [126:143]:
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                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info('Error metrics for group {}'.format(group))
                regression_metrics(y_filt, yhat, w)

        te = np.zeros((X.shape[0], self.t_groups.shape[0]))
        for i, group in enumerate(self.t_groups):
            te[:, i] = yhat_ts[group] - yhat_cs[group]

        if not return_components:
            return te
        else:
            return te, yhat_cs, yhat_ts

    def fit_predict(self, X, treatment, y, return_ci=False, n_bootstraps=1000, bootstrap_size=10000,
                    return_components=False, verbose=True):
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