A Flexible Defense Against the Winner's Curse
Tijana Zrnic, William Fithian

TL;DR
This paper introduces the zoom correction, a flexible statistical method for valid inference on the top-performing candidate, addressing the winner's curse across various fields and settings.
Contribution
The authors propose a novel, adaptable approach called zoom correction that corrects for selection bias in winner inference in both parametric and nonparametric contexts.
Findings
Effective correction for winner's curse demonstrated
Applicable to top k winners and near-winners
Handles arbitrary dependencies between candidates
Abstract
Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often interested in the population performance of the model that performs best empirically. However, cherry-picking the best candidate leads to the winner's curse: the observed performance for the winner is biased upwards, rendering conclusions based on standard measures of uncertainty invalid. We introduce the zoom correction, a novel approach for valid inference on the winner. Our method is flexible: it can be employed in both parametric and nonparametric settings, can handle arbitrary dependencies between candidates, and automatically…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Explainable Artificial Intelligence (XAI)
