class_docstr: Wrapper class for estimators from the causalml library.
  
  Supports additional parameters as listed below. For specific
  parameters of each estimator, refer to the CausalML docs.
class_docstr_tok: ["Wrapper","class","for","estimators","from","the","causalml","library",".","Supports","additional","parameters","as","listed","below",".","For","specific","parameters","of","each","estimator",",","refer","to","the","CausalML","docs","."]
code: def construct_symbolic_estimator(self, estimand):
      expr = "b: " + ",".join(estimand.outcome_variable) + "~"
      # TODO we are conditioning on a postive treatment
      # TODO create an expression corresponding to each estimator used
      var_list = (
          estimand.treatment_variable
          + estimand.get_backdoor_variables()
      )
      expr += "+".join(var_list)
      return expr
  
file_path: dowhy/dowhy/causal_estimators/causalml.py
imports: import inspect
  import logging
  from importlib import import_module
  from typing import Any, List, Optional, Protocol, Union
  from warnings import warn
  import pandas
  from dowhy.causal_estimator import CausalEstimate, CausalEstimator
  from dowhy.causal_identifier import IdentifiedEstimand
label_desc: Use image node_id 5 for calling the Causalml obj's underlying member method code with example usage: obj.construct_symbolic_estimator(estimand) and returns: expr