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PreprintarXiv licencearXiv

Integration of external predictions for efficient estimation of the risk ratio

  • Victoria Mezger
  • Nils Krüger
  • Georg Hahn
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Abstract

We consider the augmentation of randomized experiments or trials with data from observational studies for the purpose of improving statistical precision. In particular, we focus on the Hybrid Augmented Inverse Probability Weighting estimator, designed to integrate predictions from several foundation models while preserving valid statistical inference. In this article, we extend the Augmented Inverse Probability Weighting framework to the estimation of the risk ratio, the quotient of the absolute risk of an exposed to an unexposed group. Our approach allows one to use information from black-box foundation models trained on external and possibly unstructured data, yielding an estimator of the risk ratio whose asymptotic variance is never larger than the one of the default estimator based on experimental data alone.

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Cite

BibTeXRIS
BibTeX
@misc{mezger2026integration,
  title = {{Integration of external predictions for efficient estimation of the risk ratio}},
  author = {Victoria Mezger and Nils Krüger and Georg Hahn},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.00769},
  eprint = {2610.00769},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.00769},
}
RIS
TY  - UNPB
TI  - Integration of external predictions for efficient estimation of the risk ratio
AU  - Victoria Mezger
AU  - Nils Krüger
AU  - Georg Hahn
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.00769
UR  - https://arxiv.org/abs/2610.00769
AB  - We consider the augmentation of randomized experiments or trials with data from observational studies for the purpose of improving statistical precision. In particular, we focus on the Hybrid Augmented Inverse Probability Weighting estimator, designed to integrate predictions from several foundation models while preserving valid statistical inference. In this article, we extend the Augmented Inverse Probability Weighting framework to the estimation of the risk ratio, the quotient of the absolute risk of an exposed to an unexposed group. Our approach allows one to use information from black-box foundation models trained on external and possibly unstructured data, yielding an estimator of the risk ratio whose asymptotic variance is never larger than the one of the default estimator based on experimental data alone.
ER  -