In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes domains such as healthcare, employment, and criminal justice, raising concerns about the fairness behavior of these systems. Existing works in fair ML cover tasks such as bias detection, fair prediction, and fair decision-making, but largely focus on static settings. At the same time, fairness in temporal contexts, particularly survival/time-to-event (TTE) analysis, remains relatively underexplored, with current approaches to fair survival analysis adopting statistical fairness definitions, which, even with unlimited data, cannot disentangle the causal mechanisms that generate disparities. To address this gap, we develop a causal framework for fairness in TTE analysis, enabling the decomposition of disparities in survival into contributions from direct, indirect, and spurious pathways. This provides a human-understandable explanation of why disparities arise and how they evolve over time. Our non-parametric approach proceeds in four steps: (1) formalizing the necessary assumptions about censoring and lack of confounding using a graphical model; (2) recovering the conditional survival function given covariates; (3) applying the Causal Reduction Theorem to reframe the problem in a form amenable to causal pathway decomposition; (4) estimating the effects efficiently. Finally, our approach is used to analyze the temporal evolution of racial disparities in outcome after admission to an intensive care unit (ICU).

翻译：在数据驱动时代，大规模数据集通常借助机器学习（ML）和人工智能（AI）进行收集与分析，以支持医疗、就业和刑事司法等高风险领域的决策，这引发了对这些系统公平性的担忧。现有的公平ML研究涵盖了偏差检测、公平预测和公平决策等任务，但主要集中于静态场景。与此同时，时序背景下的公平性，尤其是生存分析/时间至事件（TTE）分析领域，仍相对未被充分探索。当前公平生存分析的方法采用统计学公平定义，即使拥有无限数据，也无法厘清产生差异的因果机制。为填补这一空白，我们针对TTE分析中的公平性构建了一个因果框架，能够将生存差异分解为直接路径、间接路径和虚假路径的贡献，从而以人类可理解的方式解释差异为何产生以及如何随时间演变。我们的非参数方法包含四个步骤：（1）利用图模型形式化关于删失和缺乏混杂的必要假设；（2）恢复给定协变量条件下的条件生存函数；（3）应用因果约简定理将问题重构为适于因果路径分解的形式；（4）高效估计效应。最后，我们运用该方法分析重症监护室（ICU）入院后种族差异的时间演变。