ATE for multiple continuous treatments

[ankur-tutlani]

We have a scenario where we want to assess the impact of two continuous treatments, T1 and T2 on outcome Y. We have some common causes X1, X2, and X3 all continuous. Target Y is also continuous. The need is to calculate the ATE with custom values of control and treatment values.

Questions we want to address:

1. Impact of T1 on Y
2. Impact of T2 on Y
3. Impact of T1 and T2 together on Y (as T1 and T2 might have some influence on each other).

What should be the causal graphs which can answer these questions.
For questions 1 and 2, I am assuming below graphs can answer these.

For question 3, I started with following graph.

I am looking to try below causal methods.
backdoor.linear_regression
backdoor.econml.dml.DML
backdoor.econml.dml.LinearDML
backdoor.econml.dml.KernelDML

I have got some results by using backdoor.linear_regression. But the results from using double ML (linear, DML) models do not make sense. Its giving outputs which is unrealistic. I am getting this warning while running double ML models. Not sure if I am specifying the values correctly in control_value, treatment_value?

A scalar was specified but there are multiple treatments; the same value will be used for each treatment. Consider specifyingall treatments, or using the const_marginal_effect method.

Below is the code which I tried for above causal structure to answer question 3. control_value_list and treatment_value_list contains the values for treatments T1, and T2 in the same order with which it was supplied while creating causal model object. e.g. control_value_list=[7,9] and treatment_value_list=[10,5]. Means for treatment T1, we want ATE with control value as 7 and treatment value as 10. And for treatment T2, we want control value as 9, and treatment value as 5.

model=CausalModel(
    data = data,
    treatment=['T1','T2'],
    outcome='Y',
    common_causes = ['X1','X2','X3']
    )

causal_estimate = model.estimate_effect(identified_estimand,
          method_name="backdoor.econml.dml.LinearDML",
          control_value = control_value_list,
          treatment_value = treatment_value_list,
          confidence_intervals=False,
          target_units = "ate",
                                        method_params={
                      "init_params":{'model_y':xgb.XGBRegressor(random_state=578,max_depth=3,n_estimators=100),
                                    'model_t': xgb.XGBRegressor(random_state=578,max_depth=3,n_estimators=100),
                                    'discrete_treatment' : False,
                                    # 'categories':[0,1],
                                    'random_state':587921,
                                    'cv':3
                                    
  #                                    'model_final':LassoCV(),
  #                                    'featurizer':PolynomialFeatures(degree=1, include_bias=True)
                                    },
                      "fit_params":{},
                      'num_null_simulations':399,'num_simulations':399})

Also, I am getting one ATE value from backdoor.linear_regression. But the output from backdoor.econml.dml.LinearDML are two separate values. Does the doubleML computing the ATE for 2 treatments separately? Also, I observed the code throws error when specifying confidence_intervals as True. Anything which can explain this?

Will the following causal structures answer question 3 better? Using one of the treatments as common cause along with rest of the other factors?

If we get ATEs from those 2 graphs above, can we add those and say that it addresses question 3? Or its not additive?
Are there any other recommendations to address question 3?

[github-actions]

This issue is stale because it has been open for 30 days with no activity.

[anurag-ae2024]

@ankur-tutlani Were you able to resolve this issue? I am also getting this warning -

A scalar was specified but there are multiple treatments; the same value will be used for each treatment. Consider specifyingall treatments, or using the const_marginal_effect method.

[ankur-tutlani]

@anurag-ae2024 Yes this is due to how econml accepts as input. If there is more than 1 treatment, then the input to T, T0 and T1 all should be pandas dataframe if your input in pandas dataframe. E.g. if control value you want to test as 10, and treatment as 15, then for T0 dataframe, all rows for that treatment column should be 10 and similarly for T1 dataframe all rows for that treatment column should be 15. Replicate the same thing for other treatment features also.
I had to run this directly inside econml code, and not wrapped inside dowhy.