AI 中文总结
该研究将因果生存森林与负对照结合，提出NC-CSF用于生存分析的异质性处理效应估计，经模拟与临床数据集验证，其可降低偏差、提升鲁棒性并揭示新的处理效应异质性模式。
AI 中文摘要
我们研究存在删失结局与未测量混杂的观察性生存研究中的异质性处理效应（HTE）估计。我们将因果生存森林（CSF）与来自代理因果推断的负对照（NC）相结合，提出负对照因果生存森林（NC-CSF），这是一种用于生存分析的灵活非参数HTE学习器。我们的方法采用包含代理变量与奈曼正交化的损失函数来训练随机森林，从而缓解未观测混杂带来的偏差并提升对干扰估计的鲁棒性。通过涵盖不同混杂水平、代理相关性与删失机制的大量模拟，我们证明NC-CSF相较于现有基线方法大幅降低了偏差与估计误差。我们还在多个临床数据集上验证了该方法的实际效用，其既确认了若干现有结论，也揭示了新的可解释处理效应异质性模式。为便于实际应用，我们提供了NC-CSF的端到端Python实现，该实现仔细处理了干扰估计与截断等实现细节。
英文摘要
We study heterogeneous treatment-effect (HTE) estimation in observational survival studies commonly associated with both censored outcomes and unmeasured confounding. We integrate causal survival forests (CSF) with negative controls (NC) from proximal causal inference and introduce Negative Control Causal Survival Forests (NC-CSF), a flexible nonparametric HTE learner for survival analysis. Our approach uses a loss that incorporates proxy variables and Neyman orthogonalization to train the random forest, thereby mitigating bias from unobserved confounding and gaining robustness to nuisance estimation. Through extensive simulations spanning varying levels of confounding, proxy relevance, and censoring mechanisms, we demonstrate that NC-CSF substantially reduces bias and estimation error relative to existing baselines. We further demonstrate the practical utility of our method on various clinical datasets, where it confirms several existing findings and also reveals new interpretable patterns of treatment-effect heterogeneity. To facilitate practical use, we provide an end-to-end Python implementation of NC-CSF that carefully handles implementation details such as nuisance estimation and clipping.
Comments41 pages, 18 figures, 18 tables
