mlsanityData to 7 Oct 2026

Robust Counterfactual Explanations in Machine Learning: A Survey

arXiv 2402.01928 ↗First posted 2 Feb 2024cs.LG cs.AIcited by 29

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Abstract (arXiv)

Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CEs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CEs. Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this survey, we review works in the rapidly growing area of robust CEs and perform an in-depth analysis of the forms of robustness they consider. We also discuss existing solutions and their limitations, providing a solid foundation for future developments.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 43 of its 57 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

43 of 57 references.

References · 43

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  3. 2019Predictive Multiplicity in Classificationcited by 162
  4. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  5. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  6. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  7. 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
  8. 2020On Counterfactual Explanations under Predictive Multiplicitycited by 73
  9. 2020On the Fairness of Causal Algorithmic Recoursecited by 69
  10. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
  11. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  12. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
31 more
  1. 2019Equalizing Recourse across Groupscited by 51
  2. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  3. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  4. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  5. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  6. 2021Consistent Counterfactuals for Deep Modelscited by 37
  7. 2020Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shiftscited by 34
  8. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
  9. 2022Formalising the Robustness of Counterfactual Explanations for Neural Networkscited by 24
  10. 2021Selective Ensembles for Consistent Predictionscited by 23
  11. 2020Scaling Guarantees for Nearest Counterfactual Explanationscited by 22
  12. 2022On the Trade-Off between Actionable Explanations and the Right to be Forgottencited by 20
  13. 2023Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisationcited by 20
  14. 2022Counterfactual Plans under Distributional Ambiguitycited by 19
  15. 2023Finding Regions of Counterfactual Explanations via Robust Optimizationcited by 19
  16. 2022Robust Bayesian Recoursecited by 18
  17. 2023Promoting Counterfactual Robustness through Diversitycited by 17
  18. 2023Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)cited by 15
  19. 2023Generating robust counterfactual explanationscited by 13
  20. 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
  21. 2023Robustness Implies Fairness in Causal Algorithmic Recoursecited by 12
  22. 2023Distributionally Robust Recourse Actioncited by 12
  23. 2022RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Modelcited by 11
  24. 2023Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgottencited by 10
  25. 2022FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanationscited by 6
  26. 2023Bayesian Hierarchical Models for Counterfactual Estimationcited by 6
  27. 2023Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbationscited by 6
  28. 2021Robust Explanations for Private Support Vector Machinescited by 4
  29. 2023Coverage-Validity-Aware Algorithmic Recoursecited by 4
  30. 2023Algorithmic Recourse with Missing Valuescited by 2
  31. 2023T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systemscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 8; 2026: 14.

29 papers.

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  1. 2024Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Changecited by 10
  2. 2024Time Can Invalidate Algorithmic Recoursecited by 8
  3. 2024Interval Abstractions for Robust Counterfactual Explanationscited by 7
  4. 2024Rigorous Probabilistic Guarantees for Robust Counterfactual Explanationscited by 6
  5. 2025RobustX: Robust Counterfactual Explanations Made Easycited by 6
  6. 2024Learning-Augmented Robust Algorithmic Recoursecited by 4
  7. 2025Counterfactual Explanations for Model Ensembles Using Entropic Risk Measurescited by 4
  8. 2025Faster Verified Explanations for Neural Networkscited by 4
  9. 2024Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inferencecited by 3
  10. 2025Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimizationcited by 3
  11. 2025Optimal Robust Recourse with Lp-Bounded Model Changecited by 3
  12. 2025Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AIcited by 2
17 more
  1. 2024Learning Actionable Counterfactual Explanations in Large State Spacescited by 1
  2. 2025Argumentative Ensembling for Robust Recourse under Model Multiplicitycited by 1
  3. 2025Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoderscited by 1
  4. 2026Provably Robust Bayesian Counterfactual Explanations under Model Changescited by 1
  5. 2026Axiomatic Foundations of Counterfactual Explanationscited by 1
  6. 2026The Role of Causality in Algorithmic Recoursecited by 1
  7. 2026The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanationscited by 0
  8. 2026GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networkscited by 0
  9. 2026Evaluating Counterfactual Explanation Methods on Incomplete Inputscited by 0
  10. 2026A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanationscited by 0
  11. 2026Counterfactual Explanations Under Concept Driftcited by 0
  12. 2026Improving Medical Communication using Rubric-Guided Counterfactual Recommendationscited by 0
  13. 2026A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networkscited by 0
  14. 2026Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japancited by 0
  15. 2026A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluationcited by 0
  16. 2026Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanationscited by 0
  17. 2026Algorithmic Recourse Under Competitioncited by 0