SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
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 .
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Shahriar Noroozizadeh
claimed_verified
2026-03-13T08:12:02.990Z
6553c5f424601ffa6674d8ea
https://cdn-avatars.huggingface.co/v1/production/uploads/6553c5f424601ffa6674d8ea/6uCQZlvPNT6NLrA0bvFv6.png
Shahriar
snoroozi
user
snoroozi
69b3bfe12ad6c5d4cf486c2e
Xiaobin Shen
claimed_verified
2026-03-13T16:50:13.584Z
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Xiaobin Shen
xiaobins1846
user
xiaobins1846
69b3bfe12ad6c5d4cf486c2f
Jeremy C. Weiss
69b3bfe12ad6c5d4cf486c30
George H. Chen
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
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 .
SurvHTE-Bench presents the first comprehensive benchmark for heterogeneous treatment effects estimation with censored survival data, featuring synthetic, semi-synthetic, and real-world datasets with varying causal assumptions and survival dynamics.
heterogeneous treatment effects right-censored survival data precision medicine individualized policy-making causal survival forests survival meta-learners outcome imputation approaches synthetic datasets semi-synthetic datasets twin study HIV clinical trial