class_docstr: Result of causal analysis.
class_docstr_tok: ["Result","of","causal","analysis","."]
code: def _create_policy(
      self,
      X_test,
      treatment_feature,
      treatment_cost,
      alpha,
      max_tree_depth,
      min_tree_leaf_samples,
  ):
      local_policies = self.causal_analysis.individualized_policy(
          X_test,
          treatment_feature,
          treatment_costs=treatment_cost,
          alpha=alpha,
      )
  
      tree = self.causal_analysis._policy_tree_output(
          X_test,
          treatment_feature,
          treatment_costs=treatment_cost,
          max_depth=max_tree_depth,
          min_samples_leaf=min_tree_leaf_samples,
          alpha=alpha,
      )
  
      return {
          ResultAttributes.TREATMENT_FEATURE: treatment_feature,
          ResultAttributes.CONTROL_TREATMENT: tree.control_name,
          ResultAttributes.LOCAL_POLICIES: local_policies,
          ResultAttributes.POLICY_GAINS: {
              ResultAttributes.RECOMMENDED_POLICY_GAINS: tree.policy_value,
              ResultAttributes.TREATMENT_GAINS: tree.always_treat,
          },
          ResultAttributes.POLICY_TREE: tree.tree_dictionary,
      }
  
imports: import json
  import re
  import uuid
  from pathlib import Path
  from typing import Any, List, Optional, Tuple, Union
  import pandas
  import semver
  from responsibleai._interfaces import CausalConfig
  from responsibleai._interfaces import CausalData, CausalPolicy, CausalPolicyGains, CausalPolicyTreeInternal, CausalPolicyTreeLeaf, ComparisonTypes
  from responsibleai._internal.constants import FileFormats, SerializationAttributes
  from responsibleai._tools.causal.causal_config import CausalConfig
  from responsibleai._tools.causal.causal_constants import ResultAttributes
  from responsibleai._tools.shared.base_result import BaseResult
  from responsibleai._tools.shared.versions import CausalVersions
label_desc: Use image node_id 9 for calling the CausalResult obj's underlying member method code with example usage: obj._create_policy(X_test, treatment_feature, treatment_cost, alpha, max_tree_depth, min_tree_leaf_samples) and returns: dict