作者：Yang, Yachong; Kuchibhotla, Arun Kumar; Tchetgen, Eric Tchetgen
作者单位：University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania
摘要：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 ana...
