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Cited by 10
Counterfactual Explanation of Machine Learning Survival Models

Understanding Survival Models Through Counterfactual Explanations
Abdallah Alabdallah, Jakub Jakubowski, Sepideh Pashami, Szymon Bobek, Mattias Ohlsson, Thorsteinn Rögnvaldsson, Grzegorz J. Nalepa
https://doi.org/10.1007/978-3-031-63772-8_28
Book:  Lecture Notes in Computer Science (Computational Science – ICCS 2024) Volume 14835 (2024), p. 310
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma, Varich Boonsanong, Minh Hoang, Keegan Hines, John Dickerson, Chirag Shah
https://doi.org/10.1145/3677119
Journal:  ACM Computing Surveys (2024)
Counterfactual explanations and how to find them: literature review and benchmarking
Riccardo Guidotti
https://doi.org/10.1007/s10618-022-00831-6
Journal:  Data Mining and Knowledge Discovery (2022)
MS-CPFI: A model-agnostic Counterfactual Perturbation Feature Importance algorithm for interpreting black-box Multi-State models
Aziliz Cottin, Marine Zulian, Nicolas Pécuchet, Agathe Guilloux, Sandrine Katsahian
https://doi.org/10.1016/j.artmed.2023.102741
Journal:  Artificial Intelligence in Medicine Volume 147 (2024), p. 102741
Model‐agnostic explanations for survival prediction models
Krithika Suresh, Carsten Görg, Debashis Ghosh
https://doi.org/10.1002/sim.10057
Journal:  Statistics in Medicine Volume 43, Issue 11 (2024), p. 2161
Note on the bi-risk discrete time risk model with income rate two
Andrius Grigutis, Artur Nakliuda
https://doi.org/10.15559/22-VMSTA209
Journal:  Modern Stochastics: Theory and Applications (2022), p. 401
Recommendation Algorithm Based on Survival Action Rules
Marek Hermansa, Marek Sikora, Beata Sikora, Łukasz Wróbel
https://doi.org/10.3390/app14072939
Journal:  Applied Sciences Volume 14, Issue 7 (2024), p. 2939
SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration
Germain Garcia-Zanabria, Daniel A. Gutierrez-Pachas, Guillermo Camara-Chavez, Jorge Poco, Erick Gomez-Nieto
https://doi.org/10.3390/app12125785
Journal:  Applied Sciences Volume 12, Issue 12 (2022), p. 5785
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek
https://doi.org/10.1016/j.knosys.2022.110234
Journal:  Knowledge-Based Systems Volume 262 (2023), p. 110234
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N Wright, Przemysław Biecek, Jonathan Wren
https://doi.org/10.1093/bioinformatics/btad723
Journal:  Bioinformatics Volume 39, Issue 12 (2023)


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