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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

Japanese

時間の異なる治療の下で時間の異なる治療データから異なる生存治療効果(HSTEs)を推定することは、医学などの分野で重要である(例えば、治療のシーケンスが生存確率の面でより優れていることを決定すること)。しかし、このタスクは、治療の決定、患者の特徴、検閲の確率がしばしば一緒に進化し、治療と検閲の両方のための順序調整を必要とするため、困難である。既存の生存学習者は主に単一のベースライン治療の任務を考慮し、長期的な治療効果の学習者は通常非検閲の結果を仮定する。この論文では、我々は時間の異なる治療と歴史依存検閲の間に異なる生存治療効果のための新しい二重強力な学習者を導

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

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