class_docstr: Compute effect of treatment using a generalized linear model such as logistic regression.
  
  Implementation uses statsmodels.api.GLM.
  Needs an additional parameter, "glm_family" to be specified in method_params. The value of this parameter can be any valid statsmodels.api families object. For example, to use logistic regression, specify "glm_family" as statsmodels.api.families.Binomial().
code: def construct_symbolic_estimator(self, estimand):
      expr = (
          "b: " + ",".join(estimand.outcome_variable) + "~" + "Sigmoid("
      )
      var_list = (
          estimand.treatment_variable
          + estimand.get_backdoor_variables()
      )
      expr += "+".join(var_list)
      if self._effect_modifier_names:
          interaction_terms = [
              "{0}*{1}".format(x[0], x[1])
              for x in itertools.product(
                  estimand.treatment_variable,
                  self._effect_modifier_names,
              )
          ]
          expr += "+" + "+".join(interaction_terms)
      expr += ")"
      return expr
  
file_path: dowhy/dowhy/causal_estimators/generalized_linear_model_estimator.py
imports: import itertools
  from typing import Any, List, Optional, Union
  import pandas
  import statsmodels.api
  from dowhy.causal_estimator import CausalEstimator
  from dowhy.causal_estimators.regression_estimator import RegressionEstimator
  from dowhy.causal_identifier import IdentifiedEstimand
label_desc: Use image node_id 5 for calling the GeneralizedLinearModelEstimator obj's underlying member method code with example usage: obj.construct_symbolic_estimator(estimand) and returns: expr