Prediction-Powered Inference with Inverse Probability Weighting
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915884867518464 |
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| author | Datta, Jyotishka Polson, Nicholas G. |
| author_facet | Datta, Jyotishka Polson, Nicholas G. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction-Powered Inference with Inverse Probability Weighting Datta, Jyotishka Polson, Nicholas G. Machine Learning 62D10, 62F10, 62-02 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. |
| title | Prediction-Powered Inference with Inverse Probability Weighting |
| topic | Machine Learning 62D10, 62F10, 62-02 |
| url | https://arxiv.org/abs/2508.10149 |