causalml/inference/meta/rlearner.py [235:250]:
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        if p is None:
            p = self.propensity
        else:
            check_p_conditions(p, self.t_groups)

        if isinstance(p, np.ndarray):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        ate = np.zeros(self.t_groups.shape[0])
        ate_lb = np.zeros(self.t_groups.shape[0])
        ate_ub = np.zeros(self.t_groups.shape[0])

        for i, group in enumerate(self.t_groups):
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causalml/inference/meta/xlearner.py [304:318]:
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        if p is None:
            p = self.propensity
        else:
            check_p_conditions(p, self.t_groups)
        if isinstance(p, np.ndarray):
            treatment_name = self.t_groups[0]
            p = {treatment_name: convert_pd_to_np(p)}
        elif isinstance(p, dict):
            p = {treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items()}

        ate = np.zeros(self.t_groups.shape[0])
        ate_lb = np.zeros(self.t_groups.shape[0])
        ate_ub = np.zeros(self.t_groups.shape[0])

        for i, group in enumerate(self.t_groups):
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