Error in the tutorial of "Finding optimal adjustment sets"

[NianzuMa]

Describe the bug
The following error appears using DoWhy 0.10.1, since the URL of tutorial of "Finding optimal adjustment sets" is for v0.10.1.

I am following the tutorial of Finding optimal adjustment sets and try to reproduce the result.

I found that this tutorial might not be updated along with the changes of the DoWhy code.
The example code reports error.

There are the some issues of the example code:

1. When I run the following code, it report the following error message:

code:

ident_eff = AutoIdentifier(
    estimand_type=EstimandType.NONPARAMETRIC_ATE,
    backdoor_adjustment=BackdoorAdjustment.BACKDOOR_EFFICIENT,
)
print(
    ident_eff.identify_effect(
        graph=G, treatment_name=treatment_name, outcome_name=outcome_name, conditional_node_names=conditional_node_names
    )
)

Error message:

Traceback (most recent call last):
  File "/home/server/user1/Bayesian_Network_Causal_Effect_Estimation_Methods/3_DoWhy/DoWhy_get_conditioning_set/v_0-10_1.py", line 72, in <module>
    ident_eff.identify_effect(
  File "/users/7/user1/anaconda3/envs/dowhy_0_10_1/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py", line 92, in identify_effect
    estimand = identify_effect_auto(
               ^^^^^^^^^^^^^^^^^^^^^
  File "/users/7/user1/anaconda3/envs/dowhy_0_10_1/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py", line 168, in identify_effect_auto
    return identify_ate_effect(
           ^^^^^^^^^^^^^^^^^^^^
  File "/users/7/user1/anaconda3/envs/dowhy_0_10_1/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py", line 258, in identify_ate_effect
    frontdoor_variables_names = identify_frontdoor(graph, treatment_name, outcome_name)
                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/users/7/user1/anaconda3/envs/dowhy_0_10_1/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py", line 767, in identify_frontdoor
    cond1 = graph.check_valid_frontdoor_set(
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/users/7/user1/anaconda3/envs/dowhy_0_10_1/lib/python3.11/site-packages/dowhy/causal_graph.py", line 461, in check_valid_frontdoor_set
    dseparated = nx.algorithms.d_separated(new_graph, set(nodes1), set(nodes2), set(candidate_nodes))
                 ^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'networkx.algorithms' has no attribute 'd_separated'. Did you mean: 'd_separation'?

2. Another theoretical question:

I have an discrete network, all nodes are variables with discrete data.

I use the following method to construct the network:

for node_name in node_name_list:
    has_parent = bool(list(networkx_directed_graph.predecessors(node_name)))
    if not has_parent:
        causal_model.set_causal_mechanism(node_name, gcm.EmpiricalDistribution())
    else:
        causal_model.set_causal_mechanism(node_name, gcm.ClassifierFCM(gcm.ml.create_logistic_regression_classifier()))
    #endif
#endfor

Following the tutorial of "Finding optimal adjustment sets",
Does the "optimal backdoor set" the features used by logistic regression to build classifier?

Thank you very much for your answer.

Steps to reproduce the behavior
Follow the code of the first example

Version information:

• DoWhy version 0.10.1

[amit-sharma]

@NianzuMa the error is because networkx renamed its d-separation method to "d_separation" from "d_separated". Can you update to dowhy v0.13 and try to rerun the code. We had added a patch for this networkx update and it should work fine.

For the second question, the optimal backdoor set uses all the potential backdoor variables available (roughly, the union of ancestors of treatment and outcome)

[NianzuMa]

Hi amit-sharma,

Thank you for your answer.

For the second question, if I use logistic regression to build classifier, what is the best way for me to find the feature set (a list of node names) used by logistic regression classifier?
My goal is to find the features set used for classifying outcome node.

In my setting, I do not have a specified treatment node.

Thank you very much for your answer.

[NianzuMa]

Hi amit-sharma,

If I switch to 0.13, if I run the following code, it will report another error:

ident_eff = AutoIdentifier(
    estimand_type=EstimandType.NONPARAMETRIC_ATE,
    backdoor_adjustment=BackdoorAdjustment.BACKDOOR_EFFICIENT,
)
print(
    ident_eff.identify_effect(
        graph=G, action_nodes=treatment_name, outcome_nodes=outcome_name, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names
    )
)

Error message:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 6
      1 ident_eff = AutoIdentifier(
      2     estimand_type=EstimandType.NONPARAMETRIC_ATE,
      3     backdoor_adjustment=BackdoorAdjustment.BACKDOOR_EFFICIENT,
      4 )
      5 print(
----> 6     ident_eff.identify_effect(
      7         graph=G, action_nodes=treatment_name, outcome_nodes=outcome_name, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names
      8     )
      9 )

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py:115, in AutoIdentifier.identify_effect(self, graph, action_nodes, outcome_nodes, observed_nodes, conditional_node_names)
    107 def identify_effect(
    108     self,
    109     graph: nx.DiGraph,
   (...)    113     conditional_node_names: List[str] = None,
    114 ):
--> 115     estimand = identify_effect_auto(
    116         graph,
    117         action_nodes,
    118         outcome_nodes,
    119         observed_nodes,
    120         self.estimand_type,
    121         conditional_node_names,
    122         self.backdoor_adjustment,
    123         self.optimize_backdoor,
    124         self.costs,
    125         self.generalized_adjustment,
    126     )
    128     estimand.identifier = self
    130     return estimand

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/dowhy/causal_identifier/auto_identifier.py:188, in identify_effect_auto(graph, action_nodes, outcome_nodes, observed_nodes, estimand_type, conditional_node_names, backdoor_adjustment, optimize_backdoor, costs, generalized_adjustment)
    185 outcome_nodes = parse_state(outcome_nodes)
    187 # First, check if there is a directed path from action to outcome
--> 188 if not has_directed_path(graph, action_nodes, outcome_nodes):
    189     logger.warning("No directed path from treatment to outcome. Causal Effect is zero.")
    190     return IdentifiedEstimand(
    191         None,
    192         treatment_variable=action_nodes,
    193         outcome_variable=outcome_nodes,
    194         no_directed_path=True,
    195     )

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/dowhy/graph.py:343, in has_directed_path(graph, action_nodes, outcome_nodes)
    341 action_node_candidates = set()
    342 for node in action_nodes:
--> 343     outcome_node_candidates.update(nx.descendants(graph, node))
    344 for node in outcome_nodes:
    345     action_node_candidates.update(nx.ancestors(graph, node))

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/networkx/utils/decorators.py:784, in argmap.__call__.<locals>.func(_argmap__wrapper, *args, **kwargs)
    783 def func(*args, __wrapper=None, **kwargs):
--> 784     return argmap._lazy_compile(__wrapper)(*args, **kwargs)

File <class 'networkx.utils.decorators.argmap'> compilation 24:3, in argmap_descendants_21(G, source, backend, **backend_kwargs)
      1 import bz2
      2 import collections
----> 3 import gzip
      4 import inspect
      5 import itertools

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/networkx/utils/backends.py:535, in _dispatchable._call_if_no_backends_installed(self, backend, *args, **kwargs)
    529 if "networkx" not in self.backends:
    530     raise NotImplementedError(
    531         f"'{self.name}' is not implemented by 'networkx' backend. "
    532         "This function is included in NetworkX as an API to dispatch to "
    533         "other backends."
    534     )
--> 535 return self.orig_func(*args, **kwargs)

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/networkx/algorithms/dag.py:73, in descendants(G, source)
     39 @nx._dispatchable
     40 def descendants(G, source):
     41     """Returns all nodes reachable from `source` in `G`.
     42 
     43     Parameters
   (...)     71     ancestors
     72     """
---> 73     return {child for parent, child in nx.bfs_edges(G, source)}

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/networkx/algorithms/dag.py:73, in <setcomp>(.0)
     39 @nx._dispatchable
     40 def descendants(G, source):
     41     """Returns all nodes reachable from `source` in `G`.
     42 
     43     Parameters
   (...)     71     ancestors
     72     """
---> 73     return {child for parent, child in nx.bfs_edges(G, source)}

File ~/anaconda3/envs/smile/lib/python3.11/site-packages/networkx/algorithms/traversal/breadth_first_search.py:187, in bfs_edges(G, source, reverse, depth_limit, sort_neighbors)
    185     successors = G.predecessors
    186 else:
--> 187     successors = G.neighbors
    189 if sort_neighbors is not None:
    190     yield from generic_bfs_edges(
    191         G, source, lambda node: iter(sort_neighbors(successors(node))), depth_limit
    192     )

AttributeError: 'CausalGraph' object has no attribute 'neighbors'

[amit-sharma]

I think this is a bug. thanks for reporting.
For now, you can change the code from graph=G to graph=G._graph and that should fix this issue.

print(
    ident_eff.identify_effect(
        graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_name, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names
    )
)

[amit-sharma]

For the second question, if I use logistic regression to build classifier, what is the best way for me to find the feature set (a list of node names) used by logistic regression classifier?

This is usually the features that you've put into the logistic regression call. If you are using a gcm model, it is the parents of the node in the graph.

[NianzuMa]

Hi amit,

I found that "Finding optimal adjustment sets" might have some bugs.
I tried in my real world dataset and it reports a error that should not exists.

The error looks like below:

Error message:
    
        networkx.exception.NetworkXError: The edge var_746-var_16 not in graph.
    

In `./debug/graph_str.txt` we can see that there are 11 edges use var_746 as source and edge (var_746, var_16) is in there.

    edge[source "var_746" target "var_16"] 
    edge[source "var_746" target "var_354"] 
    edge[source "var_746" target "var_732"] 
    edge[source "var_746" target "var_743"] 
    edge[source "var_746" target "var_752"] 
    edge[source "var_746" target "var_773"] 
    edge[source "var_746" target "var_781"] 
    edge[source "var_746" target "var_812"] 
    edge[source "var_746" target "var_836"] 
    edge[source "var_746" target "var_907"] 
    edge[source "var_746" target "var_1075"] 

In the G, which is created by

```
G = CausalGraph(
        graph=graph_str, treatment_name=treatment_name, outcome_name=outcome_variable, observed_node_names=observed_node_names
    )
```
We can see that it contains (var_746, var_16) edge:

    G._graph._adj["var_746"]
        {'var_16': {}, 'var_354': {}, 'var_732': {}, 'var_743': {}, 'var_752': {}, 'var_773': {}, 'var_781': {}, 'var_812': {}, 'var_836': {}, 'var_907': {}, 'var_1075': {}}
    
    len(G._graph._adj["var_746"])
        11

The complete code and dataset is below, it uses v0.13

In ./debug folder:

DAG.csv

graph_str.txt

treatment_name_list.json

var_name_list.json

from typing import List, Tuple
from dowhy import CausalModel
import pandas as pd
import numpy as np
import re
import numpy as np
import pandas as pd
import json
from functools import partial
from tqdm import tqdm
import numpy as np
import os
import scipy
import math
import dowhy.gcm as gcm
import networkx as nx
from sklearn.tree import DecisionTreeClassifier
import pickle
import random
import argparse
import time
import pydot
from dowhy.causal_graph import CausalGraph
from dowhy.causal_identifier import AutoIdentifier, BackdoorAdjustment, EstimandType
import datetime
import dowhy

print(f"dowhy version: {dowhy.__version__}")

def convert_nx_network_to_graph_str(nx_network):
    """
    Convert nx_network into graph_str:
        graph_str = \"""graph[directed 1 node[id "coach" label "coach"]
                                        node[id "team motivation" label "team motivation"]
                                        node[id "fitness" label "fitness"]
                                        node[id "pre-game prop" label "pre-game prop"]
                                        node[id "intra-game prop" label "intra-game prop"]
                                        node[id "neuromusc fatigue" label "neuromusc fatigue"]
                                        node[id "warm-up" label "warm-up"]
                                        node[id "previous injury" label "previous injury"]
                                        node[id "contact sport" label "contact sport"]
                                        node[id "genetics" label "genetics"]
                                        node[id "injury" label "injury"]
                                        node[id "tissue disorder" label "tissue disorder"]
                                        node[id "tissue weakness" label "tissue weakness"]
                                        edge[source "coach" target "team motivation"]
                                        edge[source "coach" target "fitness"]
                                        edge[source "fitness" target "pre-game prop"]
                                        edge[source "fitness" target "neuromusc fatigue"]
                                        edge[source "team motivation" target "warm-up"]
                                        edge[source "team motivation" target "previous injury"]
                                        edge[source "pre-game prop" target "warm-up"]
                                        edge[source "warm-up" target "intra-game prop"]
                                        edge[source "contact sport" target "previous injury"]
                                        edge[source "contact sport" target "intra-game prop"]
                                        edge[source "intra-game prop" target "injury"]
                                        edge[source "genetics" target "fitness"]
                                        edge[source "genetics" target "neuromusc fatigue"]
                                        edge[source "genetics" target "tissue disorder"]
                                        edge[source "tissue disorder" target "neuromusc fatigue"]
                                        edge[source "tissue disorder" target "tissue weakness"]
                                        edge[source "neuromusc fatigue" target "intra-game prop"]
                                        edge[source "neuromusc fatigue" target "injury"]
                                        edge[source "tissue weakness" target "injury"]
                                        ]
                        \"""
    """
    node_str = ""
    edge_str = ""
    
    for node in nx_network.nodes():
        node_str += f'node[id "{node}" label "{node}"] \n'
    #endfor
    
    for s, t in nx_network.edges():
        edge_str += f'edge[source "{s}" target "{t}"] \n'
    #endfor
    
    final_graph_str = f"graph[directed 1 {node_str} {edge_str} \n]"
        
    return final_graph_str


def debug_with_new_data(debug=True):
    """
    Error message:
    
        networkx.exception.NetworkXError: The edge var_746-var_16 not in graph.
    
    In ./debug/graph_str.txt we can see that there are 11 edges use var_746 as source. 
    edge[source "var_746" target "var_16"] 
    edge[source "var_746" target "var_354"] 
    edge[source "var_746" target "var_732"] 
    edge[source "var_746" target "var_743"] 
    edge[source "var_746" target "var_752"] 
    edge[source "var_746" target "var_773"] 
    edge[source "var_746" target "var_781"] 
    edge[source "var_746" target "var_812"] 
    edge[source "var_746" target "var_836"] 
    edge[source "var_746" target "var_907"] 
    edge[source "var_746" target "var_1075"] 
    
    In the G, which is created by
    
    ```
    G = CausalGraph(
            graph=graph_str, treatment_name=treatment_name, outcome_name=outcome_variable, observed_node_names=observed_node_names
        )
    ```
    We can see that it contains (var_746, var_16) edge:
    
    G._graph._adj["var_746"]
        {'var_16': {}, 'var_354': {}, 'var_732': {}, 'var_743': {}, 'var_752': {}, 'var_773': {}, 'var_781': {}, 'var_812': {}, 'var_836': {}, 'var_907': {}, 'var_1075': {}}
    
    len(G._graph._adj["var_746"])
        11
    """
    
    # ------- config -------
    treatment_name_list_file_path = "./debug/treatment_name_list.json"
    dag_file_path = "./debug/DAG.csv"
    var_name_list_file_path = "./debug/var_name_list.json"
    outcome_variable = "T"
    
    # load treatment name list
    with open(treatment_name_list_file_path, mode="r") as fin:
        all_treatment_name_list = json.load(fin)
    #endwith
    
    # load var name list
    with open(var_name_list_file_path, mode="r") as fin:
        var_column = json.load(fin)
        
    # load DAG
    dag = np.loadtxt(dag_file_path, delimiter=",", dtype=int)
    dag = dag.astype(bool)
    
    
    # (1) construct network for DoWhy
    edge_list = []
    ## Add edges to the network
    true_index_arr = np.argwhere(dag)
    for i in range(true_index_arr.shape[0]):
        # print(true_index_arr[i])
        x = true_index_arr[i][0]
        y = true_index_arr[i][1]
        
        if x == y:
            print(f"self loop edge: {x} -> {y}")
            break
        
        from_var_code = var_column[x]
        to_var_code = var_column[y]
        
        #print(x, from_var_code)
        #print(y, to_var_code)
        
        edge_list.append((from_var_code, to_var_code))
    #endfor
    
    # gcm stands for "Graphical Causal Models"
    # For example, causal_model = gcm.StructuralCausalModel(nx.DiGraph([('B', 'A'), ('B', 'T'), ('A', 'T')]))
    nx_directed_graph = nx.DiGraph(edge_list)
    
    # construct causal graph
    graph_str = convert_nx_network_to_graph_str(nx_directed_graph)
    
    # save graph str
    if debug:
        debug_folder = "./debug" 
        os.makedirs(debug_folder, exist_ok=True)
        graph_str_path = os.path.join(debug_folder, f"graph_str.txt")
        with open(graph_str_path, mode="w") as fout:
            fout.write(f"{graph_str}\n")
        #endwith
    #endif
    print("debug")
    
    
    observed_node_names = list(set(nx_directed_graph.nodes()) - set([outcome_variable]))
    
    # For each treatment variable in the original excel specified by Sisi, output each conditioning set
    
    treatment_name_list = []
    
    backdoor_eff_list_of_list = []
    backdoor_min_eff_list_of_list = []
    backdoor_mincost_eff_list_of_list = []
    
    
    for treatment_name in tqdm(all_treatment_name_list):
        
        print("Treatment: ", treatment_name)
        
        G = CausalGraph(
            graph=graph_str, treatment_name=treatment_name, outcome_name=outcome_variable, observed_node_names=observed_node_names
        )
        conditional_node_names = []
        
        # (1)
        print("--------------------- BackdoorAdjustment.BACKDOOR_EFFICIENT -----------------------")
        backdoor_eff_list = None
        
        
        ident_eff = AutoIdentifier(
            estimand_type=EstimandType.NONPARAMETRIC_ATE,
            backdoor_adjustment=BackdoorAdjustment.BACKDOOR_EFFICIENT,
        )
        identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
        backdoor_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
        backdoor_eff_list_of_list.append(backdoor_eff_list)
        
        
        print("--------------------- BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT -----------------------")
        backdoor_min_eff_list = None
        try:
            ident_eff = AutoIdentifier(
                estimand_type=EstimandType.NONPARAMETRIC_ATE,
                backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT,
            )
            identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
            backdoor_min_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
        except Exception as e:
            print(e)
            print(f"Error: treatment_name - {treatment_name}")
            backdoor_min_eff_list = []
        #endtry
        backdoor_min_eff_list_of_list.append(backdoor_min_eff_list)
            
        
        print("--------------------- BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT -----------------------")
        backdoor_mincost_eff_list = None
        try:
            ident_eff = AutoIdentifier(
                estimand_type=EstimandType.NONPARAMETRIC_ATE,
                backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT,
            )
            identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
            backdoor_mincost_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
            
        except Exception as e:
            print(e)
            print(f"Error: treatment_name - {treatment_name}")
            backdoor_mincost_eff_list = []
        #endtry
        backdoor_mincost_eff_list_of_list.append(backdoor_mincost_eff_list)
        
        treatment_name_list.append(treatment_name)
        
        graph_node_set_size = len(set(nx_directed_graph.nodes()))
        dataset_variable_size = len(var_column)
        print(f"graph_node_set_size: {graph_node_set_size}")
        print(f"dataset_variable_size: {dataset_variable_size}")
        
    #endfor treatment_name
    


if __name__ == "__main__":
    debug_with_new_data()

Thank you very much.

[amit-sharma]

thanks for reporting this.
@esmucler can you take a look at this issue? There may be a bug with the optimal backdoor implementation.

[esmucler]

Hi @NianzuMa could you please share a minimal reproducible example? The least amount of code possible that showcases the issue you are seeing. Thanks a lot.

[NianzuMa]

Hi Amit and @esmucler ,

Thank you for your help.
I reorganized the code to avoid any file reading. Please bear with the long python script.
The error occurs because I tried on our real world dataset.
The python script is long because the long data_str and long observation node list is in the script.
But the code logic is simplified, which could considered as minimal reproducible example.

The previous version is also simple enough, which contains the graph in networkx format, easier to check the graph.

Let me know if the following code is good for you. It reports the same error stated in my previous reply.

from typing import List, Tuple
from dowhy import CausalModel
import pandas as pd
import numpy as np
import re
import numpy as np
import pandas as pd
import json
from functools import partial
from tqdm import tqdm
import numpy as np
import os
import scipy
import math
import dowhy.gcm as gcm
import networkx as nx
from sklearn.tree import DecisionTreeClassifier
import pickle
import random
import argparse
import time
import pydot
from dowhy.causal_graph import CausalGraph
from dowhy.causal_identifier import AutoIdentifier, BackdoorAdjustment, EstimandType
import datetime
import dowhy

print(f"dowhy version: {dowhy.__version__}")


graph_str = '''graph[directed 1 node[id "var_1" label "var_1"] 
node[id "var_2" label "var_2"] 
node[id "var_3" label "var_3"] 
node[id "var_315" label "var_315"] 
node[id "var_4" label "var_4"] 
node[id "var_310" label "var_310"] 
node[id "var_6" label "var_6"] 
node[id "var_65" label "var_65"] 
node[id "var_131" label "var_131"] 
node[id "var_806" label "var_806"] 
node[id "var_7" label "var_7"] 
node[id "var_142" label "var_142"] 
node[id "var_8" label "var_8"] 
node[id "var_0" label "var_0"] 
node[id "var_25" label "var_25"] 
node[id "var_186" label "var_186"] 
node[id "var_525" label "var_525"] 
node[id "var_530" label "var_530"] 
node[id "var_565" label "var_565"] 
node[id "var_9" label "var_9"] 
node[id "var_68" label "var_68"] 
node[id "var_11" label "var_11"] 
node[id "var_581" label "var_581"] 
node[id "var_13" label "var_13"] 
node[id "var_14" label "var_14"] 
node[id "var_15" label "var_15"] 
node[id "var_16" label "var_16"] 
node[id "var_348" label "var_348"] 
node[id "var_1408" label "var_1408"] 
node[id "var_20" label "var_20"] 
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edge[source "var_1186" target "var_1382"] 
edge[source "var_1186" target "var_1606"] 
edge[source "var_1196" target "var_1522"] 
edge[source "var_1187" target "var_1167"] 
edge[source "var_1190" target "var_1229"] 
edge[source "var_1194" target "var_1518"] 
edge[source "var_1201" target "var_1198"] 
edge[source "var_1201" target "var_1535"] 
edge[source "var_1203" target "var_386"] 
edge[source "var_1203" target "var_1225"] 
edge[source "var_1203" target "var_1517"] 
edge[source "var_1204" target "var_708"] 
edge[source "var_1204" target "var_1203"] 
edge[source "var_1208" target "var_1409"] 
edge[source "var_1214" target "var_384"] 
edge[source "var_1214" target "var_1605"] 
edge[source "var_1214" target "var_1606"] 
edge[source "var_1214" target "var_1607"] 
edge[source "var_1214" target "T"] 
edge[source "var_1245" target "var_1410"] 
edge[source "var_1217" target "var_1214"] 
edge[source "var_1217" target "var_1216"] 
edge[source "var_1231" target "var_1167"] 
edge[source "var_1499" target "var_1522"] 
edge[source "var_1499" target "var_1593"] 
edge[source "var_1219" target "var_1217"] 
edge[source "var_1219" target "var_1605"] 
edge[source "var_1220" target "var_1213"] 
edge[source "var_1220" target "var_1216"] 
edge[source "var_1220" target "var_1217"] 
edge[source "var_1220" target "var_1218"] 
edge[source "var_1428" target "var_1427"] 
edge[source "var_1251" target "var_1167"] 
edge[source "var_1261" target "var_1307"] 
edge[source "var_1261" target "var_1328"] 
edge[source "var_1311" target "var_1568"] 
edge[source "var_1234" target "var_1425"] 
edge[source "var_1248" target "var_1203"] 
edge[source "var_1248" target "var_1246"] 
edge[source "var_1250" target "var_1246"] 
edge[source "var_1289" target "var_1315"] 
edge[source "var_1289" target "var_1336"] 
edge[source "var_1333" target "var_1427"] 
edge[source "var_1265" target "var_1336"] 
edge[source "var_1265" target "var_1535"] 
edge[source "var_1266" target "var_1173"] 
edge[source "var_1266" target "var_1236"] 
edge[source "var_1273" target "var_1410"] 
edge[source "var_1273" target "var_1493"] 
edge[source "var_1275" target "var_1236"] 
edge[source "var_1277" target "var_1236"] 
edge[source "var_1277" target "var_1307"] 
edge[source "var_1279" target "var_1150"] 
edge[source "var_1279" target "var_1236"] 
edge[source "var_1281" target "var_1518"] 
edge[source "var_1296" target "var_1382"] 
edge[source "var_1296" target "var_1593"] 
edge[source "var_1285" target "var_1336"] 
edge[source "var_1290" target "var_1479"] 
edge[source "var_1335" target "var_1479"] 
edge[source "var_1304" target "var_1292"] 
edge[source "var_1310" target "var_1409"] 
edge[source "var_1316" target "var_1410"] 
edge[source "var_1325" target "var_1284"] 
edge[source "var_1329" target "var_1307"] 
edge[source "var_1490" target "var_1425"] 
edge[source "var_1555" target "var_1603"] 
edge[source "var_1340" target "var_1338"] 
edge[source "var_1345" target "var_1410"] 
edge[source "var_1355" target "var_1354"] 
edge[source "var_1355" target "var_1356"] 
edge[source "var_1357" target "var_1354"] 
edge[source "var_1357" target "var_1356"] 
edge[source "var_1359" target "var_1354"] 
edge[source "var_1359" target "var_1356"] 
edge[source "var_1370" target "var_1377"] 
edge[source "var_1370" target "var_1400"] 
edge[source "var_1370" target "var_1402"] 
edge[source "var_1377" target "var_463"] 
edge[source "var_1377" target "var_465"] 
edge[source "var_1377" target "var_467"] 
edge[source "var_1377" target "var_468"] 
edge[source "var_1377" target "var_1368"] 
edge[source "var_1377" target "var_1376"] 
edge[source "var_1377" target "var_1382"] 
edge[source "var_1377" target "var_1396"] 
edge[source "var_1377" target "var_1397"] 
edge[source "var_1377" target "var_1402"] 
edge[source "var_1377" target "var_1416"] 
edge[source "var_1377" target "var_1616"] 
edge[source "var_1371" target "var_1370"] 
edge[source "var_1373" target "var_1370"] 
edge[source "var_1510" target "var_1511"] 
edge[source "var_1384" target "var_1398"] 
edge[source "var_1384" target "var_1504"] 
edge[source "var_1384" target "var_1505"] 
edge[source "var_1398" target "var_1522"] 
edge[source "var_1385" target "var_1384"] 
edge[source "var_1386" target "var_467"] 
edge[source "var_1386" target "var_696"] 
edge[source "var_1386" target "var_697"] 
edge[source "var_1386" target "var_1384"] 
edge[source "var_1386" target "var_1387"] 
edge[source "var_1386" target "var_1402"] 
edge[source "var_1387" target "var_1384"] 
edge[source "var_1388" target "var_1377"] 
edge[source "var_1388" target "var_1382"] 
edge[source "var_1388" target "var_1400"] 
edge[source "var_1419" target "var_1375"] 
edge[source "var_1419" target "var_1417"] 
edge[source "var_1419" target "var_1418"] 
edge[source "var_1419" target "var_1422"] 
edge[source "var_1419" target "var_1425"] 
edge[source "var_1415" target "var_1425"] 
edge[source "var_1417" target "var_1415"] 
edge[source "var_1418" target "var_1415"] 
edge[source "var_1429" target "var_1427"] 
edge[source "var_1436" target "var_1497"] 
edge[source "var_1443" target "var_1456"] 
edge[source "var_1448" target "var_1447"] 
edge[source "var_1469" target "var_1468"] 
edge[source "var_1482" target "var_1479"] 
edge[source "var_1491" target "var_1378"] 
edge[source "var_1491" target "var_1497"] 
edge[source "var_1491" target "var_1522"] 
edge[source "var_1491" target "var_1535"] 
edge[source "var_1602" target "var_695"] 
edge[source "var_1525" target "var_495"] 
edge[source "var_1525" target "var_1508"] 
edge[source "var_1547" target "var_1518"] 
edge[source "var_1523" target "var_1525"] 
edge[source "var_1527" target "var_1379"] 
edge[source "var_1531" target "var_1525"] 
edge[source "var_1533" target "var_1425"] 
edge[source "var_1533" target "var_1525"] 
edge[source "var_1534" target "var_1525"] 
edge[source "var_1569" target "var_1520"] 
edge[source "var_1574" target "var_1565"] 
edge[source "var_1583" target "var_1584"] 
edge[source "var_1585" target "var_1584"] 
edge[source "var_1595" target "var_1594"] 
edge[source "var_1597" target "var_701"] 
edge[source "var_1597" target "var_1575"] 
edge[source "var_1597" target "var_1594"] 
edge[source "var_1601" target "var_695"] 
edge[source "var_1601" target "var_1343"] 
 
]
'''


def debug_with_new_data():
    """
    Error message:
    
        networkx.exception.NetworkXError: The edge var_746-var_16 not in graph.
    
    In graph_str, we can see that there are 11 edges use var_746 as source. 
    edge[source "var_746" target "var_16"] 
    edge[source "var_746" target "var_354"] 
    edge[source "var_746" target "var_732"] 
    edge[source "var_746" target "var_743"] 
    edge[source "var_746" target "var_752"] 
    edge[source "var_746" target "var_773"] 
    edge[source "var_746" target "var_781"] 
    edge[source "var_746" target "var_812"] 
    edge[source "var_746" target "var_836"] 
    edge[source "var_746" target "var_907"] 
    edge[source "var_746" target "var_1075"] 
    
    In the G, which is created by
    
    ```
    G = CausalGraph(
            graph=graph_str, treatment_name=treatment_name, outcome_name=outcome_variable, observed_node_names=observed_node_names
        )
    ```
    We can see that it contains (var_746, var_16) edge:
    
    G._graph._adj["var_746"]
        {'var_16': {}, 'var_354': {}, 'var_732': {}, 'var_743': {}, 'var_752': {}, 'var_773': {}, 'var_781': {}, 'var_812': {}, 'var_836': {}, 'var_907': {}, 'var_1075': {}}
    
    len(G._graph._adj["var_746"])
        11
    """
    
    # ------- config -------
    outcome_variable = "T"
    treatment_name = "var_746"
    observed_node_names = ["var_95", "var_1601", "var_1007", "var_1026", "var_558", "var_1128", "var_391", "var_897", "var_1234", "var_419", "var_1504", "var_87", "var_434", "var_228", "var_358", "var_1429", "var_1171", "var_1214", "var_1568", "var_1018", "var_384", "var_542", "var_1545", "var_1282", "var_1056", "var_1038", "var_496", "var_919", "var_1192", "var_316", "var_952", "var_1327", "var_917", "var_1448", "var_904", "var_1552", "var_1450", "var_490", "var_1502", "var_1391", "var_1156", "var_327", "var_233", "var_1580", "var_1169", "var_1542", "var_1322", "var_224", "var_1321", "var_169", "var_393", "var_460", "var_1423", "var_932", "var_1015", "var_809", "var_1547", "var_133", "var_1470", "var_1478", "var_1464", "var_1099", "var_162", "var_1451", "var_1526", "var_1200", "var_1388", "var_573", "var_1318", "var_1385", "var_571", "var_1460", "var_605", "var_299", "var_1378", "var_1146", "var_1241", "var_1548", "var_167", "var_528", "var_1066", "var_1123", "var_352", "var_946", "var_636", "var_1359", "var_1443", "var_748", "var_820", "var_372", "var_124", "var_1422", "var_1571", "var_333", "var_183", "var_107", "var_1461", "var_8", "var_1150", "var_1167", "var_1426", "var_1129", "var_1404", "var_440", "var_1249", "var_1319", "var_1266", "var_887", "var_530", "var_763", "var_863", "var_1006", "var_1529", "var_881", "var_492", "var_168", "var_1076", "var_1431", "var_106", "var_1609", "var_227", "var_730", "var_949", "var_1109", "var_717", "var_269", "var_98", "var_532", "var_43", "var_1475", "var_1498", "var_218", "var_1154", "var_688", "var_1080", "var_834", "var_590", "var_652", "var_662", "var_1329", "var_1577", "var_1490", "var_88", "var_1398", "var_669", "var_1027", "var_971", "var_31", "var_1012", "var_1127", "var_1051", "var_682", "var_931", "var_777", "var_53", "var_941", "var_251", "var_1248", "var_703", "var_916", "var_215", "var_676", "var_746", "var_788", "var_607", "var_73", "var_883", "var_1", "var_1240", "var_239", "var_466", "var_611", "var_1222", "var_1193", "var_628", "var_1261", "var_1432", "var_673", "var_1537", "var_13", "var_1366", "var_870", "var_832", "var_510", "var_1158", "var_940", "var_986", "var_1554", "var_392", "var_1447", "var_144", "var_602", "var_874", "var_1344", "var_1227", "var_1506", "var_174", "var_1403", "var_680", "var_1399", "var_626", "var_398", "var_1445", "var_212", "var_1145", "var_286", "var_1400", "var_503", "var_1239", "var_377", "var_562", "var_1597", "var_230", "var_140", "var_649", "var_1114", "var_1071", "var_890", "var_49", "var_1608", "var_340", "var_994", "var_204", "var_1438", "var_547", "var_983", "var_305", "var_842", "var_1434", "var_591", "var_1419", "var_810", "var_353", "var_1104", "var_1255", "var_1346", "var_55", "var_1277", "var_687", "var_880", "var_1325", "var_1381", "var_1566", "var_601", "var_1536", "var_1195", "var_303", "var_62", "var_567", "var_728", "var_1283", "var_1334", "var_1427", "var_706", "var_1303", "var_759", "var_1407", "var_77", "var_1412", "var_19", "var_929", "var_1369", "var_128", "var_190", "var_415", "var_345", "var_527", "var_1174", "var_1122", "var_843", "var_804", "var_495", "var_159", "var_1175", "var_236", "var_826", "var_509", "var_312", "var_900", "var_332", "var_435", "var_369", "var_33", "var_1493", "var_470", "var_388", "var_1035", "var_211", "var_1367", "var_1380", "var_1463", "var_143", "var_1446", "var_811", "var_1235", "var_1251", "var_500", "var_365", "var_848", "var_306", "var_724", "var_1486", "var_1480", "var_1063", "var_1215", "var_201", "var_1452", "var_1153", "var_1414", "var_257", "var_241", "var_723", "var_191", "var_1531", "var_1226", "var_1328", "var_684", "var_461", "var_954", "var_1584", "var_426", "var_993", "var_1273", "var_1331", "var_1147", "var_787", "var_294", "var_1096", "var_1052", "var_1005", "var_1247", "var_892", "var_1315", "var_458", "var_103", "var_613", "var_664", "var_555", "var_68", "var_205", "var_89", "var_1196", "var_1236", "var_1185", "var_1291", "var_764", "var_1340", "var_1563", "var_1287", "var_776", "var_28", "var_1100", "var_351", "var_112", "var_165", "var_1495", "var_996", "var_335", "var_785", "var_364", "var_648", "var_1245", "var_1424", "var_773", "var_66", "var_232", "var_1121", "var_867", "var_1271", "var_976", "var_1383", "var_244", "var_550", "var_868", "var_1416", "var_373", "var_1467", "var_483", "var_1024", "var_1086", "var_953", "var_697", "var_184", "var_886", "var_101", "var_324", "var_355", "var_924", "var_289", "var_1095", "var_14", "var_925", "var_1428", "var_967", "var_862", "var_1218", "var_693", "var_1069", "var_1253", "var_936", "var_906", "var_130", "var_1025", "var_837", "var_489", "var_625", "var_656", "var_453", "var_295", "var_302", "var_1579", "var_465", "var_634", "var_487", "var_1411", "var_1356", "var_796", "var_866", "var_872", "var_963", "var_517", "var_805", "var_1546", "var_935", "var_718", "var_1064", "var_1517", "var_1454", "var_1259", "var_735", "var_895", "var_282", "var_499", "var_334", "var_548", "var_752", "var_22", "var_1272", "var_922", "var_284", "var_1163", "var_859", "var_15", "var_1252", "var_1550", "var_672", "var_108", "var_1341", "var_1456", "var_795", "var_1323", "var_891", "var_582", "var_518", "var_828", "var_583", "var_566", "var_1549", "var_1268", "var_799", "var_111", "var_1473", "var_981", "var_1339", "var_1444", "var_389", "var_418", "var_627", "var_462", "var_11", "var_1354", "var_869", "var_209", "var_1201", "var_674", "var_1059", "var_609", "var_153", "var_727", "var_23", "var_425", "var_469", "var_399", "var_17", "var_714", "var_670", "var_878", "var_485", "var_537", "var_599", "var_1275", "var_109", "var_170", "var_457", "var_1476", "var_1559", "var_1610", "var_839", "var_488", "var_1543", "var_1126", "var_347", "var_781", "var_1503", "var_504", "var_1418", "var_449", "var_1487", "var_222", "var_1332", "var_410", "var_1588", "var_1039", "var_188", "var_769", "var_136", "var_1530", "var_1135", "var_1524", "var_1538", "var_1402", "var_304", "var_1420", "var_800", "var_1362", "var_1459", "var_1085", "var_846", "var_1058", "var_104", "var_729", "var_94", "var_208", "var_123", "var_539", "var_844", "var_888", "var_565", "var_292", "var_1525", "var_281", "var_1151", "var_1453", "var_632", "var_157", "var_655", "var_1188", "var_1278", "var_424", "var_598", "var_575", "var_1540", "var_988", "var_189", "var_1292", "var_152", "var_1592", "var_923", "var_1141", "var_1120", "var_852", "var_1293", "var_1511", "var_1199", "var_1238", "var_671", "var_1551", "var_172", "var_1205", "var_1060", "var_497", "var_29", "var_125", "var_1324", "var_884", "var_642", "var_6", "var_1425", "var_618", "var_1612", "var_594", "var_608", "var_1483", "var_1570", "var_1569", "var_1223", "var_1522", "var_534", "var_20", "var_1102", "var_1289", "var_1284", "var_1182", "var_783", "var_1350", "var_277", "var_1574", "var_141", "var_741", "var_1439", "var_857", "var_246", "var_818", "var_966", "var_291", "var_707", "var_390", "var_875", "var_849", "var_840", "var_525", "var_679", "var_576", "var_695", "var_1112", "var_349", "var_1009", "var_1162", "var_1405", "var_1373", "var_920", "var_173", "var_134", "var_1161", "var_1474", "var_1604", "var_1357", "var_600", "var_164", "var_1316", "var_1371", "var_486", "var_46", "var_118", "var_838", "var_979", "var_762", "var_1589", "var_360", "var_382", "var_226", "var_1605", "var_1013", "var_346", "var_464", "var_1440", "var_978", "var_1237", "var_1387", "var_758", "var_980", "var_229", "var_833", "var_835", "var_595", "var_899", "var_1585", "var_430", "var_1111", "var_207", "var_1140", "var_448", "var_533", "var_1520", "var_1482", "var_1596", "var_1512", "var_948", "var_1246", "var_1157", "var_1178", "var_1465", "var_1320", "var_1124", "var_972", "var_1087", "var_1368", "var_597", "var_266", "var_580", "var_54", "var_263", "var_1176", "var_1016", "var_1050", "var_819", "var_798", "var_659", "var_905", "var_1308", "var_1312", "var_1116", "var_9", "var_640", "var_950", "var_30", "var_1555", "var_1384", "var_1544", "var_366", "var_1462", "var_1004", "var_397", "var_1131", "var_115", "var_756", "var_45", "var_1307", "var_423", "var_802", "var_1231", "var_564", "var_1516", "var_711", "var_791", "var_296", "var_1314", "var_831", "var_192", "var_1377", "var_1189", "var_841", "var_726", "var_1152", "var_1093", "var_853", "var_965", "var_612", "var_225", "var_1479", "var_775", "var_792", "var_526", "var_1262", "var_1364", "var_543", "var_1578", "var_61", "var_1499", "var_378", "var_615", "var_1149", "var_1598", "var_1079", "var_1003", "var_1110", "var_1296", "var_468", "var_1037", "var_1591", "var_1372", "var_1330", "var_478", "var_667", "var_1022", "var_1397", "var_1139", "var_65", "var_514", "var_1119", "var_293", "var_412", "var_311", "var_116", "var_829", "var_160", "var_1136", "var_772", "var_1363", "var_1355", "var_556", "var_1276", "var_126", "var_1435", "var_1561", "var_298", "var_148", "var_908", "var_1594", "var_1270", "var_847", "var_882", "var_145", "var_357", "var_1081", "var_1396", "var_663", "var_1097", "var_309", "var_780", "var_1505", "var_1225", "var_1224", "var_1409", "var_1108", "var_738", "var_1374", "var_1062", "var_962", "var_1075", "var_1091", "var_338", "var_1347", "var_1395", "var_1553", "var_557", "var_700", "var_1043", "var_396", "var_999", "var_1390", "var_637", "var_1030", "var_957", "var_330", "var_643", "var_1606", "var_721", "var_861", "var_1144", "var_331", "var_893", "var_113", "var_1595", "var_1105", "var_850", "var_1430", "var_1468", "var_1159", "var_262", "var_1437", "var_1041", "var_74", "var_344", "var_560", "var_1573", "var_516", "var_320", "var_1408", "var_146", "var_421", "var_813", "var_1143", "var_1118", "var_1301", "var_740", "var_1586", "var_142", "var_321", "var_991", "var_387", "var_1336", "var_1034", "var_937", "var_454", "var_1125", "var_715", "var_1212", "var_38", "var_1413", "var_1583", "var_1527", "var_1469", "var_433", "var_80", "var_341", "var_102", "var_1265", "var_1602", "var_59", "var_60", "var_1001", "var_856", "var_367", "var_1472", "var_100", "var_1457", "var_1514", "var_734", "var_934", "var_329", "var_1077", "var_1491", "var_720", "var_385", "var_1053", "var_318", "var_1089", "var_217", "var_803", "var_1148", "var_647", "var_395", "var_938", "var_577", "var_34", "var_1183", "var_1406", "var_743", "var_696", "var_654", "var_619", "var_1049", "var_816", "var_354", "var_1194", "var_604", "var_691", "var_1509", "var_48", "var_1083", "var_1564", "var_943", "var_1184", "var_1485", "var_1036", "var_722", "var_1567", "var_1094", "var_1433", "var_216", "var_1575", "var_315", "var_1164", "var_132", "var_137", "var_694", "var_1068", "var_1351", "var_638", "var_1000", "var_370", "var_1256", "var_871", "var_985", "var_1415", "var_1045", "var_1494", "var_245", "var_325", "var_790", "var_359", "var_150", "var_214", "var_1134", "var_1488", "var_411", "var_898", "var_579", "var_614", "var_1309", "var_939", "var_1477", "var_1394", "var_1519", "var_181", "var_572", "var_771", "var_1305", "var_1115", "var_1600", "var_1250", "var_639", "var_1098", "var_1264", "var_47", "var_915", "var_1481", "var_666", "var_801", "var_677", "var_498", "var_154", "var_21", "var_1177", "var_1065", "var_1562", "var_690", "var_998", "var_361", "var_1054", "var_1358", "var_631", "var_1138", "var_992", "var_1304", "var_987", "var_918", "var_808", "var_1137", "var_909", "var_733", "var_645", "var_675", "var_310", "var_1014", "var_1335", "var_386", "var_668", "var_894", "var_969", "var_274", "var_1290", "var_704", "var_822", "var_1254", "var_326", "var_1220", "var_463", "var_1082", "var_1310", "var_661", "var_1181", "var_768", "var_914", "var_554", "var_1300", "var_383", "var_1221", "var_283", "var_416", "var_494", "var_947", "var_568", "var_1557", "var_736", "var_1286", "var_1587", "var_945", "var_574", "var_122", "var_288", "var_928", "var_1501", "var_782", "var_535", "var_1260", "var_307", "var_413", "var_479", "var_502", "var_1360", "var_1088", "var_1534", "var_158", "var_1572", "var_1500", "var_127", "var_814", "var_1565", "var_1133", "var_1338", "var_193", "var_944", "var_757", "var_4", "var_429", "var_376", "var_1166", "var_1370", "var_0", "var_570", "var_1603", "var_278", "var_90", "var_18", "var_1365", "var_903", "var_660", "var_279", "var_1615", "var_317", "var_1132", "var_1410", "var_1518", "var_93", "var_951", "var_199", "var_753", "var_1532", "var_186", "var_1607", "var_182", "var_709", "var_473", "var_1217", "var_177", "var_1207", "var_135", "var_1074", "var_731", "var_428", "var_1280", "var_912", "var_1484", "var_1342", "var_907", "var_531", "var_37", "var_1028", "var_451", "var_536", "var_287", "var_1288", "var_657", "var_1202", "var_902", "var_515", "var_1216", "var_276", "var_506", "var_708", "var_751", "var_1072", "var_1204", "var_1343", "var_926", "var_1302", "var_1510", "var_117", "var_176", "var_480", "var_789", "var_1203", "var_151", "var_339", "var_371", "var_7", "var_767", "var_851", "var_921", "var_250", "var_1257", "var_1186", "var_1243", "var_1317", "var_960", "var_1040", "var_1023", "var_1142", "var_701", "var_1229", "var_467", "var_99", "var_1379", "var_1556", "var_1117", "var_149", "var_1106", "var_1173", "var_1103", "var_1179", "var_445", "var_644", "var_737", "var_1361", "var_180", "var_973", "var_984", "var_1533", "var_1258", "var_1267", "var_450", "var_374", "var_1386", "var_927", "var_959", "var_1230", "var_1020", "var_69", "var_603", "var_681", "var_821", "var_964", "var_178", "var_622", "var_67", "var_1002", "var_1213", "var_35", "var_1392", "var_1067", "var_63", "var_1337", "var_624", "var_1541", "var_322", "var_328", "var_650", "var_1029", "var_394", "var_1593", "var_553", "var_271", "var_350", "var_760", "var_1048", "var_933", "var_138", "var_901", "var_1092", "var_786", "var_1558", "var_686", "var_702", "var_356", "var_806", "var_1471", "var_472", "var_896", "var_1576", "var_91", "var_3", "var_807", "var_794", "var_910", "var_692", "var_1311", "var_864", "var_475", "var_578", "var_845", "var_824", "var_240", "var_166", "var_405", "var_432", "var_913", "var_1055", "var_930", "var_493", "var_1441", "var_1582", "var_86", "var_876", "var_559", "var_1155", "var_26", "var_552", "var_678", "var_336", "var_1274", "var_401", "var_1198", "var_511", "var_968", "var_1614", "var_1298", "var_32", "var_689", "var_342", "var_484", "var_1209", "var_39", "var_161", "var_221", "var_1042", "var_1228", "var_105", "var_1375", "var_778", "var_1492", "var_1535", "var_699", "var_716", "var_1352", "var_1326", "var_1180", "var_1113", "var_784", "var_797", "var_815", "var_206", "var_1281", "var_219", "var_546", "var_885", "var_243", "var_1590", "var_617", "var_505", "var_501", "var_1061", "var_529", "var_308", "var_120", "var_131", "var_770", "var_1389", "var_1306", "var_1187", "var_71", "var_1033", "var_1417", "var_52", "var_1090", "var_793", "var_606", "var_745", "var_343", "var_765", "var_238", "var_1613", "var_653", "var_646", "var_581", "var_1345", "var_285", "var_75", "var_817", "var_1017", "var_375", "var_563", "var_1219", "var_455", "var_56", "var_873", "var_1046", "var_744", "var_747", "var_596", "var_1515", "var_889", "var_1333", "var_1349", "var_300", "var_830", "var_147", "var_129", "var_1382", "var_1442", "var_1616", "var_974", "var_712", "var_1210", "var_1421", "var_1130", "var_513", "var_25", "var_1523", "var_1190", "var_1581", "var_1353", "var_323", "var_538", "var_1393", "var_1233", "var_1168", "var_1508", "var_698", "var_977", "var_855", "var_1170", "var_337", "var_314", "var_1513", "var_1191", "var_44", "var_713", "var_879", "var_1279", "var_1376", "var_1539", "var_956", "var_685", "var_997", "var_549", "var_379", "var_970", "var_761", "var_507", "var_1084", "var_83", "var_1599", "var_719", "var_1070", "var_710", "var_683", "var_290", "var_990", "var_630", "var_658", "var_1497", "var_1047", "var_319", "var_482", "var_1611", "var_447", "var_766", "var_551", "var_621", "var_512", "var_540", "var_641", "var_1521", "var_623", "var_114", "var_1489", "var_1021", "var_754", "var_1019", "var_1242", "var_989", "var_827", "var_297", "var_24", "var_1032", "var_40", "var_1299", "var_519", "var_1295", "var_858", "var_200", "var_942", "var_110", "var_2", "var_1073", "var_1496", "var_196", "var_755", "var_955", "var_481", "var_982", "var_185", "var_524", "var_1436", "var_823", "var_995", "var_705", "var_1011", "var_812", "var_1449", "var_1244", "var_1232", "var_836", "var_732", "var_280", "var_1285", "var_1458", "var_16", "var_825", "var_431", "var_198", "var_665", "var_975", "var_1466", "var_197", "var_452", "var_616", "var_231", "var_1172", "var_422", "var_1165", "var_1208", "var_1401", "var_1078", "var_877", "var_561", "var_1031", "var_348", "var_1010", "var_156"]
        
    print("Treatment: ", treatment_name)
    
    G = CausalGraph(
        graph=graph_str, treatment_name=treatment_name, outcome_name=outcome_variable, observed_node_names=observed_node_names
    )
    conditional_node_names = []
    
    # (1)
    print("--------------------- BackdoorAdjustment.BACKDOOR_EFFICIENT -----------------------")
    backdoor_eff_list = None
    
    ident_eff = AutoIdentifier(
        estimand_type=EstimandType.NONPARAMETRIC_ATE,
        backdoor_adjustment=BackdoorAdjustment.BACKDOOR_EFFICIENT,
    )
    identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
    backdoor_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
    
    
    print("--------------------- BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT -----------------------")
    backdoor_min_eff_list = None
    try:
        ident_eff = AutoIdentifier(
            estimand_type=EstimandType.NONPARAMETRIC_ATE,
            backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT,
        )
        identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
        backdoor_min_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
    except Exception as e:
        print(e)
        print(f"Error: treatment_name - {treatment_name}")
        backdoor_min_eff_list = []
    #endtry
        
    
    print("--------------------- BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT -----------------------")
    backdoor_mincost_eff_list = None
    try:
        ident_eff = AutoIdentifier(
            estimand_type=EstimandType.NONPARAMETRIC_ATE,
            backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT,
        )
        identify_effect_result = ident_eff.identify_effect(graph=G._graph, action_nodes=treatment_name, outcome_nodes=outcome_variable, observed_nodes=observed_node_names, conditional_node_names=conditional_node_names)
        backdoor_mincost_eff_list = identify_effect_result.backdoor_variables["backdoor1"]
        
    except Exception as e:
        print(e)
        print(f"Error: treatment_name - {treatment_name}")
        backdoor_mincost_eff_list = []
    #endtry
    


if __name__ == "__main__":
    debug_with_new_data()