class_docstr: Class that implements different identification methods.
  
  Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided.
  
  This class is for backwards compatibility with CausalModel
  Will be deprecated in the future in favor of function call auto_identify_effect()
code: def __init__(
      self,
      estimand_type: EstimandType,
      backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT,
      optimize_backdoor: bool = False,
      costs: Optional[List] = None,
  ):
      self.estimand_type = estimand_type
      self.backdoor_adjustment = backdoor_adjustment
      self.optimize_backdoor = optimize_backdoor
      self.costs = costs
      self.logger = logging.getLogger(__name__)
  
imports: import itertools
  import logging
  from enum import Enum
  from typing import Dict, List, Optional, Union
  import networkx
  import sympy
  import sympy.stats
  from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.graph import check_dseparation, check_valid_backdoor_set, check_valid_frontdoor_set, check_valid_mediation_set, do_surgery, get_all_directed_paths, get_backdoor_paths, get_descendants, get_instruments, has_directed_path
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 1 to create a new AutoIdentifier object  with example: obj = AutoIdentifier(estimand_type, backdoor_adjustment, optimize_backdoor, costs)