SingleTreePolicyInterpreter (causal forest) Follow-Up

Hi Keith,
@kbattocchi

I had a follow-up question on the SingleTreePolicyInterpreter (causal forest) Interpreter in general. My questions are:

1. If I have 3 Y-variables (i.e. sales, retention, session duration), would be SingleTreePolicyInterpreter consider all 3 of them when building the policy tree and when calculating the [value - cost] in each leaf plus the overall "average policy gain" displayed at the top of the tree? or do I need to do this 3 times for each Y?
2. For the parameter "sample_treatment_costs", I tried using a numpy array because for each user, the cost would be different (based on their LTV). Is that possible because when I tried it, it was expecting a double scalar. Is there any work-around or must this parameter be a constant?
3. If my two Treatments are "discount" or "customer support", how do I know using the SingleTreePolicyInterpreter for which users"

- increase discounts for? (for insensitive users)
- decrease discounts for?(for sensitive users)
- increase customer support for? (for non-saavy users)
- decrease customer support for? (for saavy users)

Currently, my tree outputs only: "Discount" and "customer support" but not the direction (increase or decrease). How could I get to that? Do I need to work directly with the individual CATEs of each subject which drifts away from the SingleTreePolicyInterpreter approach?

Start with the SingleTreePolicyInterpreter entry point and its sample_treatment_costs parameter. Clarify whether multi-outcome policies, per-user treatment costs, and treatment direction are supported, then define the required API behavior and tests before implementation.