class_docstr: Takes a random subset of size sample_size of the data.
code: def sensitivity_estimate(self):
      df_new = self.df.sample(frac=self.sample_size).copy()
  
      X_new = df_new[self.inference_features].values
      p_new = df_new[self.p_col].values
      treatment_new = df_new[self.treatment_col].values
      y_new = df_new[self.outcome_col].values
  
      ate_new, ate_new_lower, ate_new_upper = self.get_ate_ci(
          X_new, p_new, treatment_new, y_new
      )
      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 SensitivitySubsetData obj's underlying member method code with example usage: obj.sensitivity_estimate() and returns: ate_new,  ate_new_lower,  ate_new_upper