mlsanityData to 7 Oct 2026

The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples

arXiv 2009.05487 ↗First posted 11 Sep 2020cs.AI cs.LGcited by 44

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Abstract (arXiv)

The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 8; 2023: 10; 2024: 10; 2025: 7; 2026: 6.

44 papers.

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  1. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  2. 2021Contrastive Explanations for Model Interpretabilitycited by 75
  3. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
  4. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  5. 2022Formalising the Robustness of Counterfactual Explanations for Neural Networkscited by 24
  6. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  7. 2022Towards Explainable Evaluation Metrics for Natural Language Generationcited by 15
  8. 2023Towards Explainable Evaluation Metrics for Machine Translationcited by 15
  9. 2022Interpretation of Black Box NLP Models: A Surveycited by 9
  10. 2023Generating collective counterfactual explanations in score-based classification via mathematical optimizationcited by 9
  11. 2023D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusioncited by 9
  12. 2023The Risks of Recourse in Binary Classificationcited by 8
32 more
  1. 2024Machine Learning Robustness: A Primercited by 8
  2. 2021DeDUCE: Generating Counterfactual Explanations Efficientlycited by 6
  3. 2023Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluationcited by 6
  4. 2024CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forestscited by 5
  5. 2023Artificial Neural Nets and the Representation of Human Conceptscited by 3
  6. 2024Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoringcited by 3
  7. 2024Trustworthy Actionable Perturbationscited by 3
  8. 2025Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimizationcited by 3
  9. 2022Testing robustness of predictions of trained classifiers against naturally occurring perturbationscited by 2
  10. 2023counterfactuals: An R Package for Counterfactual Explanation Methodscited by 2
  11. 2024Towards Non-Adversarial Algorithmic Recoursecited by 2
  12. 2024Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanationscited by 2
  13. 2024GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifierscited by 2
  14. 2026Counterfactual Explanations on Robust Perceptual Geodesicscited by 2
  15. 2026ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networkscited by 2
  16. 2023Fragility, Robustness and Antifragility in Deep Learningcited by 1
  17. 2024SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledgecited by 1
  18. 2024Do Counterfactual Examples Complicate Adversarial Training?cited by 1
  19. 2025Guidelines For The Choice Of The Baseline in XAI Attribution Methodscited by 1
  20. 2025Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantificationcited by 1
  21. 2022Clarity: an improved gradient method for producing quality visual counterfactual explanationscited by 0
  22. 2023Adaptive Adversarial Training Does Not Increase Recourse Costscited by 0
  23. 2023On the Trade-offs between Adversarial Robustness and Actionable Explanationscited by 0
  24. 2024CF-OPT: Counterfactual Explanations for Structured Predictioncited by 0
  25. 2025Countering Overfitting with Counterfactual Examplescited by 0
  26. 2025Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attackscited by 0
  27. 2025Training on Plausible Counterfactuals Removes Spurious Correlationscited by 0
  28. 2025Generalizability vs. Counterfactual Explainability Trade-Offcited by 0
  29. 2026Counterfactual Training: Teaching Models Plausible and Actionable Explanationscited by 0
  30. 2026Explainable AI Isn't Enough! Rethinking Algorithmic Contestabilitycited by 0
  31. 2026Concept-based Visual Counterfactual Explanations with Diffusion Modelscited by 0
  32. 2026From Interpretability Methods to Interpretable Modelscited by 0