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Peer-reviewedCC BYHarvard data science review

Demystifying Prediction-Powered Inference.

  • Yilin Song1
  • Dan M. Kluger2
  • Harsh Parikh3
  • Tian Gu1
  1. 1Department of Biostatistics, Columbia Mailman School of Public Health, New York, New York, United States of America
  2. 2Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
  3. 3Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
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Abstract

Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science. However, treating predictions as ground truth introduces bias, while ignoring them wastes valuable information. Prediction-Powered Inference (PPI) offers a principled framework that leverages predictions from large unlabeled datasets to improve statistical efficiency while maintaining valid inference through explicit bias correction using a smaller labeled subset. Despite its potential, the growing number of PPI variants and their subtle distinctions make it challenging for practitioners to determine when and how to apply these methods responsibly. This paper demystifies PPI by synthesizing its theoretical foundations, methodological extensions, connections to existing statistics literature, and diagnostic tools into a unified practical workflow. Using the MOSAIKS housing price data, we show that PPI variants produce tighter confidence intervals than complete-case analysis, but that double-dipping, i.e. reusing training data for inference, leads to anti-conservative confidence intervals and below-nominal coverage. Under missing-not-at-random mechanisms, all methods, including classical inference using only labeled data, yield biased estimates. We provide a decision flowchart linking assumption violations to appropriate PPI variants, a summary table of representative methods, and practical diagnostic strategies for evaluating core assumptions. By framing PPI as a general recipe rather than a single estimator, this work bridges methodological innovation and applied practice, helping researchers responsibly integrate predictions into valid inference.

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Cite

BibTeXRIS
BibTeX
@article{song2026demystifying,
  title = {{Demystifying Prediction-Powered Inference.}},
  author = {Yilin Song and Dan M. Kluger and Harsh Parikh and Tian Gu},
  journal = {Harvard data science review},
  year = {2026},
  month = oct,
  doi = {10.1162/99608f92.f95a1dd2},
  url = {https://europepmc.org/article/MED/42921300},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Demystifying Prediction-Powered Inference.
AU  - Yilin Song
AU  - Dan M. Kluger
AU  - Harsh Parikh
AU  - Tian Gu
PY  - 2026
DA  - 2026/10/08
JO  - Harvard data science review
DO  - 10.1162/99608f92.f95a1dd2
UR  - https://europepmc.org/article/MED/42921300
AB  - Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science. However, treating predictions as ground truth introduces bias, while ignoring them wastes valuable information. Prediction-Powered Inference (PPI) offers a principled framework that leverages predictions from large unlabeled datasets to improve statistical efficiency while maintaining valid inference through explicit bias correction using a smaller labeled subset. Despite its potential, the growing number of PPI variants and their subtle distinctions make it challenging for practitioners to determine when and how to apply these methods responsibly. This paper demystifies PPI by synthesizing its theoretical foundations, methodological extensions, connections to existing statistics literature, and diagnostic tools into a unified practical workflow. Using the MOSAIKS housing price data, we show that PPI variants produce tighter confidence intervals than complete-case analysis, but that double-dipping, i.e. reusing training data for inference, leads to anti-conservative confidence intervals and below-nominal coverage. Under missing-not-at-random mechanisms, all methods, including classical inference using only labeled data, yield biased estimates. We provide a decision flowchart linking assumption violations to appropriate PPI variants, a summary table of representative methods, and practical diagnostic strategies for evaluating core assumptions. By framing PPI as a general recipe rather than a single estimator, this work bridges methodological innovation and applied practice, helping researchers responsibly integrate predictions into valid inference.
SN  - 2688-8513
SN  - 2644-2353
ER  -