Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli, Niklas Koenen, Marvin N. Wright, Julia Herbinger
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 separating 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 a interaction- and time-aware interpretability framework for survival modeling, with broad applicability across time-to-event prediction tasks.
BibTeX
@inproceedings{
langbein2026functional,
title={Functional Decomposition and Shapley Interactions for Interpreting Survival Models},
author={Sophie Hanna Langbein and Hubert Baniecki and Fabian Fumagalli and Niklas Koenen and Marvin N. Wright and Julia Herbinger},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=SldP4LGjdz}
}