Extending Prediction-Powered Inference through Conformal Prediction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908600559992832 |
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| author | Csillag, Daniel Dall'Antonia, Pedro Struchiner, Claudio José Goedert, Guilherme Tegoni |
| author_facet | Csillag, Daniel Dall'Antonia, Pedro Struchiner, Claudio José Goedert, Guilherme Tegoni |
| contents | Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods with such guarantees is generally an arduous process, and has to be done case by case. In this paper, we resolve this issue by connecting prediction-powered inference with conformal prediction: by performing imputation through a calibrated conformal set-predictor, we attain validity while achieving additional guarantees in a natural manner. We instantiate our procedure for the inference of means, Z- and M-estimation, as well as e-values and e-value-based procedures. Furthermore, in the case of e-values, ours is the first general prediction-powered procedure that operates off-line. We demonstrate these advantages by applying our method on private and time-series data. Both tasks are nontrivial within the standard prediction-powered framework but become natural under our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extending Prediction-Powered Inference through Conformal Prediction Csillag, Daniel Dall'Antonia, Pedro Struchiner, Claudio José Goedert, Guilherme Tegoni Methodology Machine Learning Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods with such guarantees is generally an arduous process, and has to be done case by case. In this paper, we resolve this issue by connecting prediction-powered inference with conformal prediction: by performing imputation through a calibrated conformal set-predictor, we attain validity while achieving additional guarantees in a natural manner. We instantiate our procedure for the inference of means, Z- and M-estimation, as well as e-values and e-value-based procedures. Furthermore, in the case of e-values, ours is the first general prediction-powered procedure that operates off-line. We demonstrate these advantages by applying our method on private and time-series data. Both tasks are nontrivial within the standard prediction-powered framework but become natural under our method. |
| title | Extending Prediction-Powered Inference through Conformal Prediction |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2510.16166 |