Design-based conformal prediction
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Abstract (arXiv)
Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage. Conformal methods are an active research topic in statistics and machine learning, but only recently have they been extended to non-exchangeable data. In this paper, we invite survey methodologists to begin using and contributing to conformal methods. We introduce how conformal prediction can be applied to data from several common complex sample survey designs, under a framework of design-based inference for a finite population, and we point out gaps where survey methodologists could fruitfully apply their expertise. Our simulations empirically bear out the theoretical guarantees of finite-sample coverage, and our real-data example demonstrates how conformal prediction can be applied to complex sample survey data in practice.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 2 Mar 2023.
Builds on
26 of 71 references.
References · 26
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14 more
- 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 53
- 2021Scalable Scene Flow from Point Clouds in the Real Worldcited by 49
- 2021Improving Conditional Coverage via Orthogonal Quantile Regressioncited by 48
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- 2022Optimal Conformal Prediction for Small Areascited by 5
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Citing papers per year: 2026: 2.
2 papers.