Can we compare performance of DML estimators to DR Estimators based on the output of score method?
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Description
Hi,
Thanks for the great package and super helpful documentation.
I am working on a problem for estimating HTE's for a binary treatment and tried a couple of DML as well as DR estimators. I noticed that that DR estimators score on a validation set are 5 times greater than that of DML estimator scores .
Does that mean DML estimators are relatively better for my problem and why? Or output of score() in DML or DR can only be used to compare models within the respective classes? Also, what's the best metric to use for model selection? for e.g. Rscorer()
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Research direction
Start with the score() documentation for LinearDRLearner and LinearDML, then read the RScorer documentation and the surrounding estimator evaluation guidance. Done means clarifying whether these scores are comparable across estimator classes and identifying the documented metric or procedure recommended for model selection.
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Assessment
- Tech stack
- python
- Domain
- analytics, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 28/100