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arXiv · 2602.16505

Functional Decomposition and Shapley Interactions for Interpreting Survival Models

Sophie Hanna Langbein·Hubert Baniecki·Fabian Fumagalli·Niklas Koenen·Marvin N. Wright·Julia Herbinger
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Abstract

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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Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli, Niklas Koenen, Marvin N. Wright, Julia Herbinger. 2026-02-18. Functional Decomposition and Shapley Interactions for Interpreting Survival Models. https://arxiv.org/abs/2602.16505

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