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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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    References listed on IDEAS

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    1. Yi‐Hau Chen & Hung Chen, 2000. "A unified approach to regression analysis under double‐sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 449-460.
    2. Jennifer Alix-García & Daniel L. Millimet, 2023. "Remotely Incorrect? Accounting for Nonclassical Measurement Error in Satellite Data on Deforestation," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 10(5), pages 1335-1367.
    3. Rolf, Esther & Proctor, Jonathan & Carleton, Tamma & Bolliger, Ian & Shankar, Vaishaal & Ishihara, Miyabi & Recht, Benjamin & Hsiang, Solomon, 2021. "A generalizable and accessible approach to machine learning with global satellite imagery," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6fg976bb, Department of Agricultural & Resource Economics, UC Berkeley.
    4. Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2021. "A generalizable and accessible approach to machine learning with global satellite imagery," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    5. Shu Yang & Peng Ding, 2020. "Combining Multiple Observational Data Sources to Estimate Causal Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1540-1554, July.
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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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