Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting poses unique challenges for HTE estimation due to censoring, unobserved counterfactuals, and complex identification assumptions. Despite recent advances, from Causal Survival Forests to survival meta-learners and outcome imputation approaches, evaluation practices remain fragmented and inconsistent. We introduce SurvHTE-Bench, the first comprehensive benchmark for HTE estimation with censored outcomes. The benchmark spans (i) a modular suite of synthetic datasets with known ground truth, systematically varying causal assumptions and survival dynamics, (ii) semi-synthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study (with known ground truth) and from an HIV clinical trial. Across synthetic, semi-synthetic, and real-world settings, we provide the first rigorous comparison of survival HTE methods under diverse conditions and realistic assumption violations. SurvHTE-Bench establishes a foundation for fair, reproducible, and extensible evaluation of causal survival methods. The data and code of our benchmark are available at: https://github.com/Shahriarnz14/SurvHTE-Bench .

翻译：从右删失生存数据中估计异质性处理效应（HTE）在精准医疗和个性化政策制定等高风险应用中至关重要。然而，由于删失、未观测到的反事实以及复杂的识别假设，生存分析环境为HTE估计带来了独特的挑战。尽管从因果生存森林到生存元学习器及结果插补方法等领域已取得最新进展，但评估实践仍然零散且不一致。我们提出了SurvHTE-Bench，这是首个针对含删失结果的HTE估计的综合性基准。该基准涵盖：（i）一套模块化的合成数据集，其具有已知的真实情况，系统地变化因果假设与生存动态；（ii）半合成数据集，将真实世界协变量与模拟处理及结果配对；（iii）来自双胞胎研究（具有已知真实情况）和一项HIV临床试验的真实世界数据集。在合成、半合成及真实世界设置下，我们首次在不同条件及现实假设违背下对生存HTE方法进行了严格比较。SurvHTE-Bench为因果生存方法的公平、可复现及可扩展评估奠定了基础。本基准的数据与代码可在以下网址获取：https://github.com/Shahriarnz14/SurvHTE-Bench。