Functional Decomposition and Shapley Interactions for Interpreting Survival Models
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866912911875637248 |
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| author | Langbein, Sophie Hanna Baniecki, Hubert Fumagalli, Fabian Koenen, Niklas Wright, Marvin N. Herbinger, Julia |
| author_facet | Langbein, Sophie Hanna Baniecki, Hubert Fumagalli, Fabian Koenen, Niklas Wright, Marvin N. Herbinger, Julia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16505 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Functional Decomposition and Shapley Interactions for Interpreting Survival Models Langbein, Sophie Hanna Baniecki, Hubert Fumagalli, Fabian Koenen, Niklas Wright, Marvin N. Herbinger, Julia Machine Learning 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. |
| title | Functional Decomposition and Shapley Interactions for Interpreting Survival Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.16505 |