AI RESEARCH
Prediction-Powered Inference with Inverse Probability Weighting
arXiv CS.LG
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ArXi:2508.10149v2 Announce Type: replace-cross 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 H\'ajek-style corrections.