Doubly robust calibration of prediction sets under covariate shift

• 成果类型: Article
• 署名作者: Yang, Yachong; Kuchibhotla, Arun Kumar; Tchetgen, Eric Tchetgen
• 署名单位: University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkae009
• 发表日期: 2024
• 页码: 943-965
• 关键词: nonparametric-estimation; inference; regression; efficient

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 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.