Can Treatment(T) itself be Effect Modifier(X) in DML estimators?
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Description
Hi there, thanks for your great job for creating this awesome package. I've been learning sample notebooks. It seems like CausalForestDML can catch nonparametric heterogeneity perfectly when Xs are other feature without T. My question is what if X contain T or X is T? For example theta(T) = 1*(T<10) and Y=theta(T)*T+....... I've create some experimental dataset, CausalForestDML performs poorly at this situation. My guess is since X can perfectly predict T, so T residual will be 0 at the first stage of DML, so maybe DML is not a good choice. If that's true, what are the alternative estimators that can handle this problem?
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Research direction
The issue names CausalForestDML and DML but no source file, test, or entry point. Start by reproducing the reported experimental dataset and examining the first-stage treatment residuals; done would require a documented determination of whether treatment can also serve as an effect modifier and which estimator, if any, supports that case.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100