SingleTreePolicyInterpreter (causal forest) Follow-Up

[titubs]

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?

[kbattocchi]

The interpreter will consider all 3 outcomes; however, the target is just the simple average of them, which may not be appropriate if they don't have commensurable scales. Thus, you might want to either rescale the outputs yourself or create separate interpreters for each outcome, depending on what you're actually trying to learn.

You should be able to pass individual treatment costs - if you have two continuous treatments then these should be an n-by-2 array of marginal costs of each treatment per individual. If this isn't working, please provide a small repro.

With multiple treatments, the policy interpreter is telling you which treatment (if any) has the biggest positive effect on the (average) outcome - the direction is always assumed to be positive.

[fobembe]

@kbattocchi, @titubs

Can you please enlighten me on how you generated sample_treated_cost in the singletreepolicyinterpreter parameter? I would be glad to have your feedback. I am working on a project that implements a 5% discount for the consumers, I am thinking if 0.05 is my sample_treatment_cost??