[2508.10149] Prediction-Powered Inference with Inverse Probability Weighting

Statistics > Machine Learning arXiv:2508.10149 (stat) [Submitted on 13 Aug 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Prediction-Powered Inference with Inverse Probability Weighting Authors:Jyotishka Datta, Nicholas G. Polson View a PDF of the paper titled Prediction-Powered Inference with Inverse Probability Weighting, by Jyotishka Datta and Nicholas G. Polson View PDF HTML (experimental) Abstract:Prediction-powered inference (PPI) is a recent framework for valid statistical inference with partially labeled data, combining model-based predictions on a large unlabeled set with bias correction from a smaller labeled subset. Building on existing PPI results under covariate shift, we show that PPI rectification admits a direct design-based interpretation, and that informative labeling can be handled naturally by Horvitz--Thompson and Hájek-style corrections. This connection unites design-based survey sampling ideas with modern prediction-assisted inference, yielding estimators that remain valid when labeling probabilities vary across units. We consider the common setting where the inclusion probabilities are not known but estimated from a correctly specified model. In simulations, the performance of IPW-adjusted PPI with estimated propensities closely matches the known-probability case, retaining both nominal coverage and the variance-reduction benefits of PPI. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) MSC classes: 62D10, 62F1...