Off-Policy Evaluation and Learning for Survival Outcomes under Censoring

2026-03-24

AI Summary

This work addresses the challenge of off-policy evaluation under right-censored survival outcomes, where existing methods are prone to systematic bias and yield inaccurate policy value estimates. To mitigate this issue, the study introduces inverse probability of censoring weighting (IPCW) into off-policy evaluation for the first time, proposing two novel estimators—IPCW-IPS and IPCW-DR—that are both unbiased and doubly robust, effectively correcting for censoring-induced bias. Furthermore, the proposed framework naturally extends to policy optimization under budget constraints. Experimental results on both synthetic and real-world datasets demonstrate that the method substantially improves the accuracy of policy evaluation and enhances learning performance in censored environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Optimizing survival outcomes, such as patient survival or customer retention, is a critical objective in data-driven decision-making. Off-Policy Evaluation~(OPE) provides a powerful framework for assessing such decision-making policies using logged data alone, without the need for costly or risky online experiments in high-stakes applications. However, typical estimators are not designed to handle right-censored survival outcomes, as they ignore unobserved survival times beyond the censoring time, leading to systematic underestimation of the true policy performance. To address this issue, we propose a novel framework for OPE and Off-Policy Learning~(OPL) tailored for survival outcomes under censoring. Specifically, we introduce IPCW-IPS and IPCW-DR, which employ the Inverse Probability of Censoring Weighting technique to explicitly deal with censoring bias. We theoretically establish that our estimators are unbiased and that IPCW-DR achieves double robustness, ensuring consistency if either the propensity score or the outcome model is correct. Furthermore, we extend this framework to constrained OPL to optimize policy value under budget constraints. We demonstrate the effectiveness of our proposed methods through simulation studies and illustrate their practical impacts using public real-world data for both evaluation and learning tasks.

Problem

• Off-Policy Evaluation
• Survival Outcomes
• Censoring
• Right-Censoring
• Policy Learning

Innovation

• Off-Policy Evaluation
• Survival Analysis
• Censoring
• Inverse Probability of Censoring Weighting
• Double Robustness