class_docstr: A Sensitivity Check class to support Placebo Treatment, Irrelevant Additional Confounder
  and Subset validation refutation methods to verify causal inference.
  
  Reference: https://github.com/microsoft/dowhy/blob/master/dowhy/causal_refuters/
docstr: Return the confidence intervals for treatment effects prediction.
  
  Args:
      X (np.matrix): a feature matrix
      p (np.array): a propensity score vector between 0 and 1
      treatment (np.array): a treatment vector (1 if treated, otherwise 0)
      y (np.array): an outcome vector
  Returns:
      (numpy.ndarray): Mean and confidence interval (LB, UB) of the ATE estimate.
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code: def get_ate_ci(self, X, p, treatment, y):
      """Return the confidence intervals for treatment effects prediction.
  
      Args:
          X (np.matrix): a feature matrix
          p (np.array): a propensity score vector between 0 and 1
          treatment (np.array): a treatment vector (1 if treated, otherwise 0)
          y (np.array): an outcome vector
      Returns:
          (numpy.ndarray): Mean and confidence interval (LB, UB) of the ATE estimate.
      """
  
      learner = self.learner
      from ..inference.meta.tlearner import BaseTLearner
  
      if isinstance(learner, BaseTLearner):
          ate, ate_lower, ate_upper = learner.estimate_ate(
              X=X, treatment=treatment, y=y
          )
      else:
          try:
              ate, ate_lower, ate_upper = learner.estimate_ate(
                  X=X, p=p, treatment=treatment, y=y
              )
          except TypeError:
              ate, ate_lower, ate_upper = learner.estimate_ate(
                  X=X, treatment=treatment, y=y, return_ci=True
              )
      return ate[0], ate_lower[0], ate_upper[0]
  
imports: import logging
  import numpy
  import pandas
  from collections import defaultdict
  import matplotlib.pyplot
  from importlib import import_module
label_desc: Use image node_id 3 for calling the Sensitivity obj's underlying member method code with example usage: obj.get_ate_ci(X, p, treatment, y) and returns: ate,  ate_lower,  ate_upper