Development plan for multi-treatment #7
Description
First of all, thank you for developing this awesome package! PySpark/H2O-based algorithms are like gospels for real world large marketing datasets! Liked Uber's CausalML few years ago, but their implementation is in Pandas/sklearn so we were not able to use it on massive dataset. This package solved that issue perfectly!
Recently I was reading the tutorial on uplift model posted by your team for Web Conference 2021 (WWW’21)(https://booking.ai/uplift-modeling-f9759e3fb51e). At the end of the deck I read about the recent paper of multiple treatment with cost optimization by Uber(https://arxiv.org/abs/1908.05372), which extend the existing R-/X-learner to a multi-treatment setting, with/without control.
If I'm not mistaken, the upliftml package currently can only handle one treatment and on control scenerio for meta-learners. Want to know if your team has any development plan to extend meta-learner to multi-treatment with/without control group? Thanks!