code: def test_cate_estimates_regression(
      self, beta, num_effect_modifiers, num_samples
  ):
      data = dowhy.datasets.linear_dataset(
          beta=beta,
          num_common_causes=2,
          num_samples=num_samples,
          num_treatments=1,
          treatment_is_binary=True,
          num_effect_modifiers=num_effect_modifiers,
      )
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          graph=data["gml_graph"],
          test_significance=None,
      )
      identified_estimand = model.identify_effect()
      linear_estimate = model.estimate_effect(
          identified_estimand,
          method_name="backdoor.linear_regression",
          control_value=0,
          treatment_value=1,
      )
      if num_effect_modifiers == 0:
          assert linear_estimate.conditional_estimates is None
      else:
          assert linear_estimate.conditional_estimates is not None
  
imports: import networkx
  import pandas
  import pytest
  from flaky import flaky
  from pytest import mark
  from sklearn import linear_model
  import dowhy
  import dowhy.datasets
  from dowhy import CausalModel
  from dowhy.causal_graph import CausalGraph
  from dowhy.gcm import ProbabilisticCausalModel, StructuralCausalModel
  from dowhy.utils.graph_operations import daggity_to_dot
label_desc: Use image node_id 7 for calling the TestCausalModel obj's underlying member method code with example usage: obj.test_cate_estimates_regression(beta, num_effect_modifiers, num_samples) without return types