MultiModelWrapper seems inconsistent with DRLearner
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Description
The docstring of DRLearner advises to use MultiModelWrapper if different models per treatment arm are desired. However, Both the docstring of DRLearner and this source code assume the baseline treatment should be represented as a T of all zeros. But MultiModelWrapper.predict function cannot handle T of all zeros. It seems that MultiModalWrapper assumes that the one-hot-encoding of T should NOT exclude the baseline treatment which contradicts with the assumption of DRLearner.
What's correct way of using MultiModelWrapper for DRLearner to handle the case of multi-treatment? Thank you!
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Research direction
Read econml/dr/_drlearner.py around line 104 and econml/utilities.py around MultiModelWrapper.predict near line 1037, then compare their treatment-encoding assumptions. Reproduce the multi-treatment case described in the issue and check the DRLearner and MultiModelWrapper documentation. Done means the supported encoding or required usage is clarified and the inconsistency is resolved.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100