class_docstr: Args
  ----
  treatment : array, shape = (num_samples, )
      An array of treatment group indicator values.
  
  control_name : string
      The name of the control condition as a string. Must be contained in the treatment array.
  
  treatment_names : list, length = cate.shape[1]
      A list of treatment group names. NB: The order of the items in the
      list must correspond to the order in which the conditional average
      treatment effect estimates are in cate_array.
  
  y_proba : array, shape = (num_samples, )
      The predicted probability of conversion using the Y ~ X model across
      the total sample.
  
  cate : array, shape = (num_samples, len(set(treatment)))
      Conditional average treatment effect estimations from any model.
  
  value : array, shape = (num_samples, )
      Value of converting each unit.
  
  conversion_cost : shape = (num_samples, len(set(treatment)))
      The cost of a treatment that is triggered if a unit converts after having been in the treatment, such as a promotion code.
  
  impression_cost : shape = (num_samples, len(set(treatment)))
     The cost of a treatment that is the same for each unit whether or not they convert, such as a cost associated with a promotion channel.
  
  
  Notes
  -----
  Because we get the conditional average treatment effects from
  cate-learners relative to the control condition, we subtract the
  cate for the unit in their actual treatment group from y_proba for that
  unit, in order to recover the control outcome. We then add the cates
  to the control outcome to obtain y_proba under each condition. These
  outcomes are counterfactual because just one of them is actually
  observed.
code: def __init__(
      self,
      treatment,
      control_name,
      treatment_names,
      y_proba,
      cate,
      value,
      conversion_cost,
      impression_cost,
      *args,
      **kwargs,
  ):
      self.treatment = treatment
      self.control_name = control_name
      self.treatment_names = treatment_names
      self.y_proba = y_proba
      self.cate = cate
      self.value = value
      self.conversion_cost = conversion_cost
      self.impression_cost = impression_cost
  
imports: import numpy
label_desc: Use image node_id 1 to create a new CounterfactualValueEstimator object  with example: obj = CounterfactualValueEstimator(treatment, control_name, treatment_names, y_proba, cate, value, conversion_cost, impression_cost)