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Multi-Objective Counterfactual Explanations

arXiv 2004.11165 ↗First posted 23 Apr 2020stat.ML cs.LGcited by 157

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Abstract (arXiv)

Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'. Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori. We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem. Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space. This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. Our approach is also model-agnostic and works for numerical and categorical input features. We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations.

Key facts. Cited by 157 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Apr 2020.

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Citing papers per year: 2021: 27; 2022: 28; 2023: 36; 2024: 25; 2025: 26; 2026: 15.

157 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 203
  2. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 86
  3. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  4. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  5. 2021Counterfactual Explainable Recommendationcited by 60
  6. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  7. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  8. 2022CLEAR: Generative Counterfactual Explanations on Graphscited by 48
  9. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  10. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 39
  11. 2021Consistent Counterfactuals for Deep Modelscited by 37
  12. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
38 more
  1. 2022On the Privacy Risks of Algorithmic Recoursecited by 32
  2. 2021ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Modelscited by 25
  3. 2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
  4. 2022Formalising the Robustness of Counterfactual Explanations for Neural Networkscited by 24
  5. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  6. 2021Counterfactual Shapley Additive Explanationscited by 22
  7. 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomenacited by 21
  8. 2022Attribution-based Explanations that Provide Recourse Cannot be Robustcited by 20
  9. 2022On the Trade-Off between Actionable Explanations and the Right to be Forgottencited by 20
  10. 2022The privacy issue of counterfactual explanations: explanation linkage attackscited by 20
  11. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  12. 2023Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAIcited by 20
  13. 2021A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanationscited by 19
  14. 2022Counterfactual Plans under Distributional Ambiguitycited by 19
  15. 2022Evaluating Feature Attribution Methods in the Image Domaincited by 19
  16. 2023Finding Regions of Counterfactual Explanations via Robust Optimizationcited by 19
  17. 2021Consequence-aware Sequential Counterfactual Generationcited by 17
  18. 2021CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanationscited by 17
  19. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 15
  20. 2021Directive Explanations for Actionable Explainability in Machine Learning Applicationscited by 14
  21. 2023Efficient XAI Techniques: A Taxonomic Surveycited by 14
  22. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  23. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 13
  24. 2022Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationcited by 12
  25. 2023Robustness Implies Fairness in Causal Algorithmic Recoursecited by 12
  26. 2023Distributionally Robust Recourse Actioncited by 12
  27. 2021Efficient Decompositional Rule Extraction for Deep Neural Networkscited by 11
  28. 2022Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Settingcited by 11
  29. 2022Generating Sparse Counterfactual Explanations For Multivariate Time Seriescited by 11
  30. 2022Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanationscited by 11
  31. 2023Achieving Diversity in Counterfactual Explanations: a Review and Discussioncited by 11
  32. 2024Interpretable Machine Learning for TabPFNcited by 11
  33. 2021Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functionscited by 10
  34. 2022Diverse Counterfactual Explanations for Anomaly Detection in Time Seriescited by 10
  35. 2023Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgottencited by 10
  36. 2022Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learningcited by 9
  37. 2022Personalized Algorithmic Recourse with Preference Elicitationcited by 9
  38. 2023Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactualscited by 9

50 most cited of 157 All 157 (JSON)