Meta-learners. Usage of confounders.
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Description
Hello! Thank you for your great lib!
I have 2 questions:
- How should we deal with confounders if we use meta-learners? Only covariates X are used in the code. Or if I know that I have confounders then I can't use meta-learners?
- Am I correct, that if I have an ability to do a stratification before the experiment, then the function g(X) (for instance, in x-learner or Domain Adapt Learner) is constant and equal to 0.5?
- Am I correct, that if we do a stratification on the confounders, then I can use meta-learners as I don't have any confounders at all? Or I should use only methods that use confounders even if I do a stratification?
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start with the documentation or entry points for meta-learners, especially the X-learner and Domain Adapt Learner, and review how g(X) is described. Clarify the treatment of confounders, the effect of prior stratification, and whether stratification changes the appropriate method; done means all three numbered questions have an authoritative answer.
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Assessment
- Tech stack
- machine-learning
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100