mlsanityData to 7 Oct 2026

Interpretable Counterfactual Explanations Guided by Prototypes

arXiv 1907.02584 ↗First posted 3 Jul 2019cs.LG stat.MLcited by 223

Not on the map. Why?

Abstract (arXiv)

We propose a fast, model agnostic method for finding interpretable counterfactual explanations of classifier predictions by using class prototypes. We show that class prototypes, obtained using either an encoder or through class specific k-d trees, significantly speed up the the search for counterfactual instances and result in more interpretable explanations. We introduce two novel metrics to quantitatively evaluate local interpretability at the instance level. We use these metrics to illustrate the effectiveness of our method on an image and tabular dataset, respectively MNIST and Breast Cancer Wisconsin (Diagnostic). The method also eliminates the computational bottleneck that arises because of numerical gradient evaluation for textitblack box models.

Key facts. Cited by 223 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 52; 2022: 43; 2023: 51; 2024: 30; 2025: 28; 2026: 18.

223 papers.

Sort:

By area:

  1. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  2. 2022OpenXAI: Towards a Transparent Evaluation of Model Explanationscited by 125
  3. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  4. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  5. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  6. 2022Diffusion Visual Counterfactual Explanationscited by 96
  7. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  8. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  9. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  10. 2022Diffusion Models for Counterfactual Explanationscited by 79
  11. 2022Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanationscited by 77
  12. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
38 more
  1. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  2. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  3. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  4. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  5. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  6. 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
  7. 2021Towards the Unification and Robustness of Perturbation and Gradient Based Explanationscited by 47
  8. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  9. 2023Adversarial Counterfactual Visual Explanationscited by 47
  10. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  11. 2021Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertaintiescited by 41
  12. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  13. 2021Consistent Counterfactuals for Deep Modelscited by 37
  14. 2023Measuring axiomatic soundness of counterfactual image modelscited by 34
  15. 2022Cycle-Consistent Counterfactuals by Latent Transformationscited by 33
  16. 2021MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networkscited by 32
  17. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
  18. 2022On the Privacy Risks of Algorithmic Recoursecited by 32
  19. 2022Diffeomorphic Counterfactuals with Generative Modelscited by 29
  20. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  21. 2021How Well do Feature Visualizations Support Causal Understanding of CNN Activations?cited by 28
  22. 2023GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanationscited by 27
  23. 2021ECINN: Efficient Counterfactuals from Invertible Neural Networkscited by 26
  24. 2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
  25. 2022Formalising the Robustness of Counterfactual Explanations for Neural Networkscited by 24
  26. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  27. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  28. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  29. 2024Benchmarking Counterfactual Image Generationcited by 22
  30. 2021CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Modelscited by 20
  31. 2022On the Trade-Off between Actionable Explanations and the Right to be Forgottencited by 20
  32. 2022VCNet: A self-explaining model for realistic counterfactual generationcited by 20
  33. 2023Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisationcited by 20
  34. 2024Counterfactual Image Editingcited by 20
  35. 2021A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Datacited by 19
  36. 2021Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimatescited by 19
  37. 2023Model Based Explanations of Concept Driftcited by 19
  38. 2021Counterfactual Explanations as Interventions in Latent Spacecited by 18

50 most cited of 223 All 223 (JSON)