提出新方法解析生存模型中特征随时间的交互作用。
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
- 将生存模型的高阶影响分解为时变与时不变部分，揭示解释失效时机。
- 扩展Shapley值用于时间索引函数，可量化随时间变化的特征交互。
- 适合关注临床预测、风险评估等需动态解释的场景。
危险度和生存函数是时间事件预测中的自然且可解释的目标，但其固有的非加性从根本上限制了标准加性解释方法。我们提出生存功能分解（SurvFD），一种用于分析机器学习生存模型中特征交互的原理性方法。通过将高阶效应分解为时变和时不变成分，SurvFD为生存解释提供了前所未有的视角，明确刻画了加性解释失效的时间与原因。基于此理论分解，我们提出SurvSHAP-IQ，将Shapley交互扩展至时间索引函数，提供了一种高阶、时变交互的实用估计器。SurvFD与SurvSHAP-IQ共同构建了一个具有交互感知和时间感知能力的生存建模可解释性框架，适用于各类时间事件预测任务。
原文摘要 · Abstract (English)
Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation methods. We introduce Survival Functional Decomposition (SurvFD), a principled approach for analyzing feature interactions in machine learning survival models. By decomposing higher-order effects into time-dependent and time-independent components, SurvFD offers a previously unrecognized perspective on survival explanations, explicitly characterizing when and why additive explanations fail. Building on this theoretical decomposition, we propose SurvSHAP-IQ, which extends Shapley interactions to time-indexed functions, providing a practical estimator for higher-order, time-dependent interactions. Together, SurvFD and SurvSHAP-IQ establish an interaction- and time-aware interpretability approach for survival modeling, with broad applicability across time-to-event prediction tasks.
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