causalml/inference/meta/slearner.py [276:292]:
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            logger.info("Bootstrap Confidence Intervals for ATE")
            ate_bootstraps = np.zeros(shape=(self.t_groups.shape[0], n_bootstraps))

            for n in tqdm(range(n_bootstraps)):
                ate_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
                ate_bootstraps[:, n] = ate_b.mean()

            ate_lower = np.percentile(
                ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1
            )
            ate_upper = np.percentile(
                ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1
            )

            # set member variables back to global (currently last bootstrapped outcome)
            self.t_groups = t_groups_global
            self._classes = _classes_global
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causalml/inference/meta/tlearner.py [278:294]:
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            logger.info("Bootstrap Confidence Intervals for ATE")
            ate_bootstraps = np.zeros(shape=(self.t_groups.shape[0], n_bootstraps))

            for n in tqdm(range(n_bootstraps)):
                ate_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
                ate_bootstraps[:, n] = ate_b.mean()

            ate_lower = np.percentile(
                ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1
            )
            ate_upper = np.percentile(
                ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1
            )

            # set member variables back to global (currently last bootstrapped outcome)
            self.t_groups = t_groups_global
            self._classes = _classes_global
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