[论文] SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimati...

论文概要

研究领域: ML
作者: Anonymous
发布时间: 2026-03-06
arXiv: 2603.05501

中文摘要

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、未观测的反事实和复杂的识别假设，生存分析环境给 HTE 估计带来了独特挑战。本文推出了 SurvHTE-Bench，这是首个针对删失结果 HTE 估计的综合基准测试。该基准涵盖：(i) 具有已知真实值的模块化合成数据集套件，(ii) 将真实世界协变量与模拟处理和结果配对的半合成数据集，以及 (iii) 来自双胞胎研究和 HIV 临床试验的真实世界数据集。

原文摘要

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. 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, (ii) semi-synthetic datasets that pair real-world covariates with simulated treatments and outcomes, and (iii) real-world datasets from a twin study and from an HIV clinical trial.

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