Conformalized Survival Counterfactuals Prediction for General Right-Censored Data

Sijie Ren, Meng Yan, Zhen Zhang, Xu Yinghui, Xinwei Sun

Keywords: Conformalized survival analysis, Counterfactual inference, General right-censored data

TL;DR: We propose a conformalized survival counterfactuals prediction method under the potential outcome framework with exact miscoverage guarantee.

Abstract: This paper aims to develop a lower prediction bound (LPB) for survival time across different treatments in the general right-censored setting. Although previous methods have utilized conformal prediction to construct the LPB, their resulting prediction sets provide only probably approximately correct (PAC)–type miscoverage guarantees rather than exact ones. To address this problem, we propose a new calibration procedure under the potential outcome framework. Under the strong ignorability assumption, we propose a reweighting scheme that can transform the problem into a weighted conformal inference problem, allowing an LPB to be obtained via quantile regression with an exact miscoverage guarantee. Furthermore, our procedure is doubly robust against model misspecification. Empirical evaluations on synthetic and real-world clinical data demonstrate the validity and informativeness of our constructed LPBs, which indicate the potential of our analytical benchmark for comparing and selecting personalized treatments.

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

Submission Number: 10078