Treatment featurizer in CausalForestDML
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Description
I’m trying to build a model for the heterogenous treatment effect using the CausalForestDML class where the nuisance functions are fitted by a CatBoost regressor. Although this works fine for a linear continuous treatment, I get an error from CatBoost when trying polynomial features for the treatment using the treatment_featurizer argument.
The CatBoost nuisance model for the treatment is receiving a multi-column target, which doesn’t seem to support. Is this the intended behavior? Can a model be fitted to each polynomial degree of the feature or do I need a multi-output regressor?
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start by reproducing the CatBoost error when CausalForestDML uses treatment_featurizer with polynomial features. Inspect how the treatment nuisance model receives the featurized treatment and determine whether the supported behavior is per-feature fitting or a multi-output regressor. Done means the intended behavior is documented or the unsupported combination is corrected and covered by a regression test.
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Assessment
- Tech stack
- machine-learning, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 42/100