# FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference

arXiv: https://arxiv.org/abs/2502.02363 · HTML: https://mlsanity.com/paper/2502.02363/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [PPI++: Efficient Prediction-Powered Inference](https://mlsanity.com/paper/2311.01453/) (2023)
- [Cross-Prediction-Powered Inference](https://mlsanity.com/paper/2309.16598/) (2023)
- [Active Statistical Inference](https://mlsanity.com/paper/2403.03208/) (2024)
- [Semi-supervised Inference: General Theory and Estimation of Means](https://mlsanity.com/paper/1606.07268/) (2016)
- [Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation](https://mlsanity.com/paper/2406.04291/) (2024)
- [Bayes-optimal prediction with frequentist coverage control](https://mlsanity.com/paper/2105.14045/) (2021)
- [On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior](https://mlsanity.com/paper/1610.05559/) (2016)
- [Bayesian Prediction-Powered Inference](https://mlsanity.com/paper/2405.06034/) (2024)
- [Adaptive multigroup confidence intervals with constant coverage](https://mlsanity.com/paper/1612.08287/) (2016)
- [Bayes-assisted Confidence Regions: Focal Point Estimator and Bounded-influence Priors](https://mlsanity.com/paper/2410.20169/) (2024)
- [Exact adaptive confidence intervals for linear regression coefficients](https://mlsanity.com/paper/1705.08331/) (2017)

## Built on by (most cited first, 7 of 7)

- [Anytime-valid, Bayes-assisted, Prediction-Powered Inference](https://mlsanity.com/paper/2505.18000/) (2025)
- [Statistical Inference under Performativity](https://mlsanity.com/paper/2505.18493/) (2025)
- [Revisiting Active Sequential Prediction-Powered Mean Estimation](https://mlsanity.com/paper/2604.18569/) (2026)
- [Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts](https://mlsanity.com/paper/2602.02229/) (2026)
- [Extending Prediction-Powered Inference through Conformal Prediction](https://mlsanity.com/paper/2510.16166/) (2025)
- [Asymptotically Log-Optimal Bayes-Assisted Confidence Sequences for Bounded Means](https://mlsanity.com/paper/2605.07964/) (2026)
- [Black-Box Assisted Regression: Phase Transitions and Minimax Optimality](https://mlsanity.com/paper/2606.25743/) (2026)

## Abstract (arXiv)

Prediction-powered inference (PPI) enables valid statistical inference by combining experimental data with machine learning predictions. When a sufficient number of high-quality predictions is available, PPI results in more accurate estimates and tighter confidence intervals than traditional methods. In this paper, we propose to inform the PPI framework with prior knowledge on the quality of the predictions. The resulting method, which we call frequentist, assisted by Bayes, PPI (FAB-PPI), improves over PPI when the observed prediction quality is likely under the prior, while maintaining its frequentist guarantees. Furthermore, when using heavy-tailed priors, FAB-PPI adaptively reverts to standard PPI in low prior probability regions. We demonstrate the benefits of FAB-PPI in real and synthetic examples.