Exploring multi-treatment (discrete) cases

[Lekunze]

Hey, I'm getting started with CATE models in econml and I have been exploring examples for discrete treatments with p > 2. Is there in-built support for these, and how can I formulate such an example? For now I have explored passing treatment values as a list (eg. [1, 0, 0..]) to estimate effects on individual treatments, and other examples passing the entire treatment set. But estimating heterogenous effect at least in the _cate_estimator.py works for binary cases, so multi-treatment examples will require re-formulation as multi-binary cases. Is this observation correct?

[kbattocchi]

Generally our estimators support only a single discrete treatment (although many estimators do support an arbitrary cardinality for that that single treatment, not just binary). So if you have multiple discrete treatments you will need to manually translate them into a format that we support, either

1. Encoding all of them as a single discrete treatment of cardinality dt_1 x dt_2 x ..., in which case each distinct combination is treated as a unique treatment for which we compute the effect
2. One-hot-encoding each treatment (dropping one of the columns to avoid colinearity), concatenating them, and treating them as if they were continuous. This should enable you to compute the (additive) effect of each of the different treatments separately.