causalml/inference/meta/slearner.py [179:195]:
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            te_bootstraps = np.zeros(
                shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)
            )

            logger.info("Bootstrap Confidence Intervals")
            for i in tqdm(range(n_bootstraps)):
                te_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
                te_bootstraps[:, :, i] = te_b

            te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
            te_upper = np.percentile(
                te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2
            )

            # 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 [184:200]:
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            te_bootstraps = np.zeros(
                shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)
            )

            logger.info("Bootstrap Confidence Intervals")
            for i in tqdm(range(n_bootstraps)):
                te_b = self.bootstrap(X, treatment, y, size=bootstrap_size)
                te_bootstraps[:, :, i] = te_b

            te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
            te_upper = np.percentile(
                te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2
            )

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