code: def one_sided_att(alpha, p, treatment):
      """One sided confounding function for the average effect of the treatment among the treated units (ATT)
  
      Reference:  Blackwell, Matthew. "A selection bias approach to sensitivity analysis
      for causal effects." Political Analysis 22.2 (2014): 169-182.
      https://www.mattblackwell.org/files/papers/causalsens.pdf
  
      Args:
          alpha (np.array): a confounding values vector
          p (np.array): a propensity score vector between 0 and 1
          treatment (np.array): a treatment vector (1 if treated, otherwise 0)
      """
      assert p.shape[0] == treatment.shape[0]
      adj = alpha * (1 - treatment)
      return adj
  
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 a global function with example usage: one_sided_att(alpha, p, treatment) and returns: adj