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A Preview of the Predict-Then-Debias Bootstrap

Author

Listed:
  • Dan M. Kluger
  • Kerri Lu
  • Tijana Zrnic
  • Sherrie Wang
  • Stephen Bates

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.

Suggested Citation

  • Dan M. Kluger & Kerri Lu & Tijana Zrnic & Sherrie Wang & Stephen Bates, 2026. "A Preview of the Predict-Then-Debias Bootstrap," AEA Papers and Proceedings, American Economic Association, vol. 116, pages 98-102, May.
    • Handle: RePEc:aea:apandp:v:116:y:2026:p:98-102
    DOI: 10.1257/pandp.20261021
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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