mlsanityData to 14 Oct 2026

Global Counterfactual Explanations: Investigations, Implementations and Improvements

arXiv 2204.06917 ↗First posted 14 Apr 2022cs.LG cs.AI cs.CYcited by 9

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Abstract (arXiv)

Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While some works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are either reliable or computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing global methods, with a focus on implementing and improving Actionable Recourse Summaries (AReS), the only known global counterfactual explanation framework for recourse.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 14 Apr 2022.

Builds on

17 of 29 references.

References · 17

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  2. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  3. 2018Actionable Recourse in Linear Classificationcited by 328
  4. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  5. 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
  6. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 144
  7. 2020A survey of algorithmic recourse: definitions, formulations, solutions, and prospectscited by 118
  8. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  9. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  10. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 86
  11. 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
  12. 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
5 more
  1. 2019FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensemblescited by 43
  2. 2021Optimal Counterfactual Explanations in Tree Ensemblescited by 36
  3. 2020BRPO: Batch Residual Policy Optimizationcited by 25
  4. 2021Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimatescited by 19
  5. 2020Explaining Groups of Points in Low-Dimensional Representationscited by 16

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Cited by year (2026 to 14 Oct)

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

9 papers.