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[2602.16505] Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Summary
This article introduces Survival Functional Decomposition (SurvFD) and SurvSHAP-IQ, innovative methods for interpreting survival models by analyzing feature interactions and their time-dependent effects.
Why It Matters
Understanding survival models is crucial in fields like healthcare and finance, where predicting time-to-event outcomes can significantly impact decision-making. The proposed methods enhance interpretability, allowing practitioners to better understand model behavior and improve predictions.
Key Takeaways
- SurvFD provides a new framework for analyzing feature interactions in survival models.
- The methods address limitations of traditional additive explanation techniques.
- SurvSHAP-IQ extends Shapley interactions to accommodate time-dependent functions.
- The approach enhances interpretability for time-to-event predictions across various applications.
- This research contributes to the growing need for transparent AI in critical decision-making areas.
Statistics > Machine Learning arXiv:2602.16505 (stat) [Submitted on 18 Feb 2026] Title:Functional Decomposition and Shapley Interactions for Interpreting Survival Models Authors:Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli, Niklas Koenen, Marvin N. Wright, Julia Herbinger View a PDF of the paper titled Functional Decomposition and Shapley Interactions for Interpreting Survival Models, by Sophie Hanna Langbein and 5 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (stat.ML); Machine...
