mlsanityData to 7 Oct 2026

Counterfactual Explanations for Machine Learning: Challenges Revisited

arXiv 2106.07756 ↗First posted 14 Jun 2021cs.LG cs.AIcited by 19

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Abstract (arXiv)

Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form ``if an input datapoint were x' instead of x, then an ML model's output would be y' instead of y.'' Counterfactual explainability for ML models has yet to see widespread adoption in industry. In this short paper, we posit reasons for this slow uptake. Leveraging recent work outlining desirable properties of CFEs and our experience running the ML wing of a model monitoring startup, we identify outstanding obstacles hindering CFE deployment in industry.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 14 Jun 2021.

Builds on

15 of 35 references.

References · 15

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  3. 2018Model Cards for Model Reportingcited by 1,068
  4. 2018Actionable Recourse in Linear Classificationcited by 327
  5. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  6. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  7. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  8. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  9. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 143
  10. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  11. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  12. 2020A survey of algorithmic recourse: definitions, formulations, solutions, and prospectscited by 118
3 more
  1. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  2. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  3. 2018Ethical Dimensions of Visualization Researchcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 6; 2024: 3; 2025: 3; 2026: 2.

19 papers.

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  1. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 12
  2. 2022Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differcited by 12
  3. 2022Interpretation of Black Box NLP Models: A Surveycited by 9
  4. 2023Preemptively Pruning Clever-Hans Strategies in Deep Neural Networkscited by 9
  5. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
  6. 2024View-based Explanations for Graph Neural Networkscited by 8
  7. 2021MCCE: Monte Carlo sampling of realistic counterfactual explanationscited by 7
  8. 2023RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learningcited by 6
  9. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  10. 2024Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoringcited by 3
  11. 2025Data and AI governance: Promoting equity, ethics, and fairness in large language modelscited by 2
  12. 2023Evaluating counterfactual explanations using Pearl's counterfactual methodcited by 1
7 more
  1. 2023Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposalcited by 1
  2. 2023T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systemscited by 1
  3. 2025Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detectioncited by 1
  4. 2025Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanationscited by 1
  5. 2026Axiomatic Foundations of Counterfactual Explanationscited by 1
  6. 2024Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectivescited by 0
  7. 2026Explaining Reinforcement Learning Decisions in Self-adaptive Systemscited by 0