Will DRPolicyForest control for confounder in Treatments that occured under different seasons?

@kbattocchi Hi Keith,
I wanted to understand how the DRPolicyForest would behave if I had 2 treatments in which one treatment is randomized data collected during December and the other treatment is randomized data collected during May.

If I were to specify confounder such as seasonal factors, would the DRPolicyForest be able to control for differences in seasons among the two treatments and make them more comparable?

For example:

• Treatment 1 from May generated $5 revenue per user
• Treatment 2 from December generated $8 revenue per user

We cannot compare $5 with $8 directly due to seasonal confounders.

Is there a way with the DRPolicyForest to show case how the adjusted figures for revenue per user would look like?
I.e. User A's observed Treatment 1 generated $4 in revenue. However, User's A de-biased revenue under Treatment 1 would be $3 (after controlling for confounder)?

Is that possible as I am only able to get the counterfactual effect values (model.predict_value(X_test)) for all treatments BUT the observed one. Or is there anything using the counterfactual values to showcase this point?

Start with the DRPolicyForest API and the model.predict_value(X_test) entry point mentioned in the issue. Determine whether seasonal confounders can be supplied and whether adjusted outcomes for the observed treatment are supported; done means providing a documented, reproducible answer or identifying the required capability.