class_docstr: Adds an irrelevant random covariate to the dataframe.
code: def sensitivity_estimate(self):
      num_rows = self.df.shape[0]
      new_data = np.random.randn(num_rows)
  
      X = self.df[self.inference_features].values
      p = self.df[self.p_col].values
      treatment = self.df[self.treatment_col].values
      y = self.df[self.outcome_col].values
      X_new = np.hstack((X, new_data.reshape((-1, 1))))
  
      ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(
          X_new, p, treatment, y
      )
      return ate_new, ate_new_lower, ate_new_upper
  
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 2 for calling the SensitivityRandomCause obj's underlying member method code with example usage: obj.sensitivity_estimate() and returns: ate_new,  ate_new_lower,  ate_new_upper