class_docstr: Base class for estimators that estimate effects based on propensity of
  treatment assignment.
  
  Supports additional parameters as listed below.
class_docstr_tok: ["Base","class","for","estimators","that","estimate","effects","based","on","propensity","of","treatment","assignment",".","Supports","additional","parameters","as","listed","below","."]
docstr: A symbolic string that conveys what each estimator does.
  For instance, linear regression is expressed as
  y ~ bx + e
docstr_tok: ["A","symbolic","string","that","conveys","what","each","estimator","does",".","For","instance",",","linear","regression","is","expressed","as","y","~","bx","+","e"]
code: def construct_symbolic_estimator(self, estimand):
      """
      A symbolic string that conveys what each estimator does.
      For instance, linear regression is expressed as
      y ~ bx + e
      """
      raise NotImplementedError
  
file_path: dowhy/dowhy/causal_estimators/propensity_score_estimator.py
imports: from typing import Any, List, Optional, Union
  import numpy
  import pandas
  from sklearn import linear_model
  from sklearn.exceptions import NotFittedError
  from dowhy.causal_estimator import CausalEstimator
  from dowhy.causal_identifier import IdentifiedEstimand
label_desc: Use image node_id 4 for calling the PropensityScoreEstimator obj's underlying member method code with example usage: obj.construct_symbolic_estimator(estimand) without return types