A Preview of the Predict-Then-Debias Bootstrap

Dan M. Kluger, Kerri Lu, Tijana Zrnic, Sherrie Wang and Stephen Bates

AEA Papers and Proceedings, 2026, vol. 116, 98-102

Abstract: Predictions from complex machine learning models are increasingly used as input data in subsequent statistical analyses, yet their errors can bias estimators and lead to invalid confidence intervals. Existing approaches attempt to remedy this issue but often impose strong assumptions or lack generality. As an alternative, we present the Predict-Then-Debias bootstrap, developed in Kluger et al. (2025). The method yields valid confidence intervals provided that a small complete sample from the population of interest is available. Its bootstrap construction applies to a broad class of estimators and can be modified to account for weighted, stratified, or clustered samples.

JEL-codes: C13 C15 C45 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1257/pandp.20261021

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