Informatica

CSDA-Vis: A (What-If-and-When) visual system for early dropout detection using counterfactual and survival analysis interactions

Germain Garcia-Zanabria, Daniel A. Gutierrez-Pachas, Jorge Poco, Erick Gomez-Nieto

https://doi.org/10.1016/j.cag.2025.104489

Journal  Computers & Graphics Volume 134 (2026), p. 104489

Causal Post-hoc XAI: Categorisation and Systematic Literature Review

Anna Rodum Bjøru, Helge Langseth, Inga Strümke, Kerstin Bach

https://doi.org/10.1007/s10994-026-07103-0

Journal  Machine Learning Volume 115, Issue 8 (2026)

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 Volume 56, Issue 12 (2024), p. 1

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 Volume 38, Issue 5 (2024), p. 2770

CoxSE: Exploring the potential of self-explaining neural networks with Cox proportional hazards model for survival analysis

Abdallah Alabdallah, Omar Hamed, Mattias Ohlsson, Thorsteinn Rögnvaldsson, Sepideh Pashami

https://doi.org/10.1016/j.knosys.2025.114996

Journal  Knowledge-Based Systems Volume 333 (2026), p. 114996

Enhancing the interpretability of non-linear proportional hazard models introducing ghost variables

Santiago Isaza-Cadavid, Santiago Ortiz, Nicolas Orozco, Héctor R. Quiceno

https://doi.org/10.1007/s00180-026-01744-4

Journal  Computational Statistics Volume 41, Issue 4 (2026)

JointLIME: An interpretation method for machine learning survival models with endogenous time‐varying covariates in credit scoring

Yujia Chen, Raffaella Calabrese, Belen Martin‐Barragan

https://doi.org/10.1111/risa.17679

Journal  Risk Analysis Volume 45, Issue 12 (2025), p. 4135

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

Machine Learning–Based Selection of Resection vs Transplant and Survival in Hepatocellular Carcinoma

Hyun Uk Kim, Ji Won Han, Pil Soo Sung, Jeong Won Jang, Seung Kew Yoon, Ho Joong Choi, Young Kyoung You

https://doi.org/10.1001/jamanetworkopen.2025.32353

Journal  JAMA Network Open Volume 8, Issue 9 (2025), p. e2532353

Machine Learning–Guided Personalized Immunochemotherapy Strategies in Intrahepatic Cholangiocarcinoma

Jun-Hao Mei, Kai Zhang, Ying Zhang, Zhen Li, Nai-Jian Ge, Xue Han, Sui-Xing Zhong, Xiu-Ping Zhang, Ping-Ping Wu, Wei-Fu Lv, Jun Wu, Jian-Xiong Wu, Chao Wang, Hui Yan, Tian Huang, Jie Liu, Kai-Zhi Jia, Yue Liu, Jin-He Guo, Rong Liu, Gao-Jun Teng, Jian Lu

https://doi.org/10.1016/j.jhepr.2026.102004

Journal  JHEP Reports (2026), p. 102004

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

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models

Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov, Vladimir A. Muliukha

https://doi.org/10.1109/ACCESS.2025.3537459

Journal  IEEE Access Volume 13 (2025), p. 24137

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

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

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)