Develops a multi-target evaluation framework for winner's curse corrections in experiments and proposes an adaptive empirical likelihood procedure that achieves asymptotically valid confidence intervals without resampling tuning.
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Valuing Winners: When and How to Correct for Selection Bias in Randomized Experiments
Develops a multi-target evaluation framework for winner's curse corrections in experiments and proposes an adaptive empirical likelihood procedure that achieves asymptotically valid confidence intervals without resampling tuning.