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Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments

Tomàs Grau Garriga, Munib Mesinovic, Luke De Bretton-Gordon, Gerard Sanz, Axel Brando, Tingting Zhu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26710 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

在不同时间的治疗中,从时间到事件数据中估计异常的生存治疗效果(HSTE)在医学等领域很重要(例如,在生存概率方面决定哪个治疗序列更好)。然而,这个任务是具有挑战性的,因为治疗决定,患者特征和审查概率往往一起演变,需要对治疗和审查进行连续调整。现有生存学习者主要考虑一个单一的基线治疗任务,而长效治疗学习者通常假设未审查的结果。在本文中,我们引入了一种新的双重强有力的学习者,用于不同期限的治疗和历史依赖的审查中的异常生存治疗效果。我们的框架将干预窗口与生存地平线分开,并为一个有限的期限所规定的治疗能力进行示范,这在自然的过程之后

Machine translation (Meta M2M100); the English original follows.

Abstract

Estimating heterogeneous survival treatment effects (HSTEs) from time-to-event data under time-varying treatments is important in fields such as medicine (e.g., to decide which sequence of treatments is better in terms of survival probability). However, this task is challenging because treatment decisions, patient characteristics, and censoring probability often evolve together, requiring sequential adjustment for both treatments and censoring. Existing survival learners mostly consider a single baseline treatment assignment, while longitudinal treatment-effect learners typically assume uncensored outcomes. In this paper, we introduce a novel doubly robust learner for heterogeneous survival treatment effects under time-varying treatments and history-dependent censoring. Our framework separates the intervention window from the survival horizon, with treatment prescribed for a finite period, after which care follows its natural course. We further establish the favorable theoretical properties of Neyman-orthogonality, rate double robustness and quasi-oracle efficiency. The framework accommodates flexible machine learning models for nuisance estimation and treatment-effect regression. We evaluate our learners through several numerical experiments, demonstrating their effectiveness.

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Topics: Censoring (clinical trials), Robustness (evolution), Computer science, Survival analysis, Artificial intelligence, Machine learning

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange