Conformal Prediction Under Covariate Shift

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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


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 the two distributions is known---or, in practice, can be estimated accurately from a set of unlabeled data (test covariate points). Our weighted extension of conformal prediction also applies more broadly, 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.