Doubly robust calibration of prediction sets under covariate shift

Conformal prediction has received tremendous attention in recent years and has offered new solutions to problems in missing data and causal inference; yet these advances have not leveraged modern semi-parametric efficiency theory for more efficient uncertainty quantification. We consider the problem of obtaining well-calibrated prediction regions that can data adaptively account for a shift in the distribution of covariates between training and test data. Under a covariate shift assumption analogous to the standard missing at random assumption, we propose a general framework based on efficient influence functions to construct well-calibrated prediction regions for the unobserved outcome in the test sample without compromising coverage.