Tuning KernelDML
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Description
I'm very interested in using econml.dml.KernelDML to compute heterogeneous treatment effects. I am wondering if you could provide some suggestions on following:
- Should we try to tune dim and bw parameters in econml.dml.KernelDML? If so, what is the best way to tune them? With standard predictive analysis we can use cross-validations, but for causal analysis not sure if this is the best way.
- To use KernelDML, should we pre-process the features (e.g., normalize features in X matrix).
Thank you!
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Research direction
Start with the econml.dml.KernelDML entry point and investigate the requested guidance on tuning dim and bw and preprocessing the X matrix. The issue names no files or tests and does not define a concrete documentation change; done would require an agreed methodological recommendation and a documented answer.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100