Confidence Intervals for ATE
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Description
I can estimate CATEs and their confidence intervals easily enough using estimator.effect(X=X, T0=T0, T1=T1) and estimator.effect_interval(X=X, T0=T0, T1=T1). However, what about (unconditional) averages treatment effects (ATEs)? I can estimate the ATE by just taking a mean over the CATEs estimator.effect(X=X, T0=T0, T1=T1).mean(), but I don't believe just taking the mean over the CATE intervals will give me valid confidence intervals for the ATE (feel free to correct me if I'm wrong or if this mean over CATE intervals gives a strictly more conservative interval that what I would get if I were to directly bootstrap ATE intervals). Is there functionality in EconML to get confidence intervals of the ATE?
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Research direction
Start with the estimator.effect and estimator.effect_interval entry points described in the issue, then review how EconML currently aggregates CATE estimates. Determine the appropriate valid confidence-interval approach for an unconditional ATE, including whether direct bootstrapping is needed; done means EconML exposes documented ATE confidence intervals.
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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
- Mostly clear
- Newbie friendliness
- 35/100