Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
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 und...
| Authors | Kohsuke Kubota, Mitsuhiro Takahashi, Yuta Saito |
|---|---|
| Primary category | stat.ME |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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