mlsanityData to 7 Oct 2026

On the computation of counterfactual explanations -- A survey

arXiv 1911.07749 ↗First posted 15 Nov 2019cs.LG cs.AI stat.MLcited by 33

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Abstract (arXiv)

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this survey we review model-specific methods for efficiently computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in literature so far.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Nov 2019.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 6; 2024: 5; 2025: 2; 2026: 2.

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  1. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  2. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  3. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  4. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  5. 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
  6. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  7. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  8. 2022Robust Bayesian Recoursecited by 18
  9. 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
  10. 2023Distributionally Robust Recourse Actioncited by 12
  11. 2024Establishing and Evaluating Trustworthy AI: Overview and Research Challengescited by 12
  12. 2022Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Settingcited by 11
21 more
  1. 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
  2. 2024Probabilistically Plausible Counterfactual Explanations with Normalizing Flowscited by 11
  3. 2025Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanationscited by 10
  4. 2022Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learningcited by 9
  5. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  6. 2022Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Boxcited by 7
  7. 2021Convex optimization for actionable & plausible counterfactual explanationscited by 6
  8. 2022FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanationscited by 6
  9. 2021Seven challenges for harmonizing explainability requirementscited by 4
  10. 2023For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAIcited by 3
  11. 2026Counterfactual Explanations on Robust Perceptual Geodesicscited by 2
  12. 2021Contrastive Explanations for Explaining Model Adaptationscited by 1
  13. 2021Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysiscited by 1
  14. 2023T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systemscited by 1
  15. 2024Explaining Learned Reward Functions with Counterfactual Trajectoriescited by 1
  16. 2024One-for-many Counterfactual Explanations by Column Generationcited by 1
  17. 2024Explainable bank failure prediction models: Counterfactual explanations to reduce the failure riskcited by 1
  18. 2022Counterfactual Explanations for Support Vector Machine Modelscited by 0
  19. 2023A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Datacited by 0
  20. 2025Counterfactual optimization for fault prevention in complex wind energy systemscited by 0
  21. 2026A Data-Driven Approach to Support Clinical Renal Replacement Therapycited by 0