class_docstr: Compute effect of treatment using linear regression.
  
  Fits a regression model for estimating the outcome using treatment(s) and confounders. For a univariate treatment, the treatment effect is equivalent to the coefficient of the treatment variable.
  
  Simple method to show the implementation of a causal inference method that can handle multiple treatments and heterogeneity in treatment. Requires a strong assumption that all relationships from (T, W) to Y are linear.
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code: def predict_fn(self, data, model, features):
      return model.predict(features)
  
file_path: dowhy/dowhy/causal_estimators/linear_regression_estimator.py
imports: import itertools
  from typing import 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 4 for calling the LinearRegressionEstimator obj's underlying member method code with example usage: obj.predict_fn(data, model, features) and returns: model