mlsanityData to 7 Oct 2026

Convex Density Constraints for Computing Plausible Counterfactual Explanations

arXiv 2002.04862 ↗First posted 12 Feb 2020cs.LG cs.AI stat.MLcited by 32

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Abstract (arXiv)

The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models. Counterfactual explanations are considered as one of the most popular techniques to explain a specific decision of a model. While the computation of "arbitrary" counterfactual explanations is well studied, it is still an open research problem how to efficiently compute plausible and feasible counterfactual explanations. We build upon recent work and propose and study a formal definition of plausible counterfactual explanations. In particular, we investigate how to use density estimators for enforcing plausibility and feasibility of counterfactual explanations. For the purpose of efficient computations, we propose convex density constraints that ensure that the resulting counterfactual is located in a region of the data space of high density.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2020.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 2; 2024: 9; 2025: 3; 2026: 4.

32 papers.

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  1. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  2. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  3. 2021Optimal Counterfactual Explanations in Tree Ensemblescited by 36
  4. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  5. 2021A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Datacited by 19
  6. 2022Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Settingcited by 11
  7. 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
  8. 2024Probabilistically Plausible Counterfactual Explanations with Normalizing Flowscited by 11
  9. 2022Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learningcited by 9
  10. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  11. 2023CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flowscited by 8
  12. 2022Explaining Reject Options of Learning Vector Quantization Classifierscited by 7
20 more
  1. 2022Model Agnostic Local Explanations of Rejectcited by 7
  2. 2021Convex optimization for actionable & plausible counterfactual explanationscited by 6
  3. 2024Generating Likely Counterfactuals Using Sum-Product Networkscited by 6
  4. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  5. 2024CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forestscited by 5
  6. 2024Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levelscited by 5
  7. 2024Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processescited by 4
  8. 2022Don't Explain Noise: Robust Counterfactuals for Randomized Ensemblescited by 2
  9. 2021Contrastive Explanations for Explaining Model Adaptationscited by 1
  10. 2021Causality-based Counterfactual Explanation for Classification Modelscited by 1
  11. 2024S-CFE: Simple Counterfactual Explanationscited by 1
  12. 2024Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainabilitycited by 1
  13. 2024Natural Counterfactuals With Necessary Backtrackingcited by 0
  14. 2025HyConEx: Hypernetwork classifier with counterfactual explanations for tabular datacited by 0
  15. 2025Interpretable Event Diagnosis in Water Distribution Networkscited by 0
  16. 2025RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanationscited by 0
  17. 2026On the Hardness of Computing Counterfactual and Semifactual Explanations in XAIcited by 0
  18. 2026A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanationscited by 0
  19. 2026CEL: Comprehensive Counterfactual Explanations Library and Benchmarkcited by 0
  20. 2026Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledgecited by 0