mlsanityData to 7 Oct 2026

SurvSHAP(t): Time-dependent explanations of machine learning survival models

arXiv 2208.11080 ↗First posted 23 Aug 2022cs.LG cs.AI stat.MLcited by 24

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Abstract (arXiv)

Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explain the survival function prediction. In this paper, we introduce SurvSHAP(t), the first time-dependent explanation that allows for interpreting survival black-box models. It is based on SHapley Additive exPlanations with solid theoretical foundations and a broad adoption among machine learning practitioners. The proposed methods aim to enhance precision diagnostics and support domain experts in making decisions. Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME. SurvSHAP(t) is model-agnostic and can be applied to all models with functional output. We provide an accessible implementation of time-dependent explanations in Python at http://github.com/MI2DataLab/survshap.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 21 of its 72 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

21 of 72 references.

References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  4. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  5. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 318
  6. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
  7. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
  8. 2021FastSHAP: Real-Time Shapley Value Estimationcited by 123
  9. 2020TimeSHAP: Explaining Recurrent Models through Sequence Perturbationscited by 73
  10. 2021Synthetic Benchmarks for Scientific Research in Explainable Machine Learningcited by 59
  11. 2014A Weighted Correlation Index for Rankings with Tiescited by 41
  12. 2020dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Pythoncited by 40
9 more
  1. 2020SurvLIME: A method for explaining machine learning survival modelscited by 34
  2. 2018DALEX: explainers for complex predictive modelscited by 28
  3. 2021Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Featurescited by 19
  4. 2022auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Datacited by 17
  5. 2021SurvNAM: The machine learning survival model explanationcited by 16
  6. 2020Counterfactual explanation of machine learning survival modelscited by 8
  7. 2020A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov boundscited by 7
  8. 2021BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo valuescited by 7
  9. 2021Variational Bayes survival analysis for unemployment modellingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 5; 2025: 3; 2026: 7.

24 papers.

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  1. 2023Deep Learning for Survival Analysis: A Reviewcited by 75
  2. 2023A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Themcited by 15
  3. 2023Interpretable machine learning for time-to-event prediction in medicine and healthcarecited by 7
  4. 2023SurvLIMEpy: A Python package implementing SurvLIMEcited by 4
  5. 2024Interpretable Machine Learning for Survival Analysiscited by 4
  6. 2024Efficient and Accurate Explanation Estimation with Distribution Compressioncited by 4
  7. 2023SurvBeX: An explanation method of the machine learning survival models based on the Beran estimatorcited by 3
  8. 2024Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysiscited by 3
  9. 2025Gradient-based Explanations for Deep Learning Survival Modelscited by 3
  10. 2023Explaining and visualizing black-box models through counterfactual pathscited by 2
  11. 2024Interpretable Prediction and Feature Selection for Survival Analysiscited by 2
  12. 2025Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanationscited by 2
12 more
  1. 2023survex: an R package for explaining machine learning survival modelscited by 1
  2. 2024CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysiscited by 1
  3. 2026Functional Decomposition and Shapley Interactions for Interpreting Survival Modelscited by 1
  4. 2023Area-norm COBRA on Conditional Survival Predictioncited by 0
  5. 2023SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Modelscited by 0
  6. 2025Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forestscited by 0
  7. 2026Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysiscited by 0
  8. 2026Explainable AI for Data-Driven Design of High-Dimensional Predictive Studiescited by 0
  9. 2026comprisk: A scikit-learn-compatible Python toolkit for competing-risks survival analysiscited by 0
  10. 2026A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputscited by 0
  11. 2026DynSHAP: Towards Explainable Dynamic Survival Analysiscited by 0
  12. 2026A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertaintycited by 0