Conformal Prediction Under Covariate Shift

Ryan J. Tibshirani; Rina Foygel Barber; Emmanuel J. Candes; Aaditya Ramdas

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arXiv · 1904.06019

Conformal Prediction Under Covariate Shift

Ryan J. Tibshirani·Rina Foygel Barber·Emmanuel J. Candes·Aaditya Ramdas

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Abstract

We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distribution-free prediction intervals for problems in which the test and training covariate distributions differ, but the likelihood ratio between these two distributions is known---or, in practice, can be estimated accurately with access to a large set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more generally, to settings in which the data satisfies a certain weighted notion of exchangeability. We discuss other potential applications of our new conformal methodology, including latent variable and missing data problems.

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BibTeX RIS

Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas. 2019-04-12. Conformal Prediction Under Covariate Shift. https://arxiv.org/abs/1904.06019

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