mlsanityData to 14 Oct 2026

Convex optimization for actionable & plausible counterfactual explanations

arXiv 2105.07630 ↗First posted 17 May 2021cs.LG cs.AIcited by 6

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Abstract (arXiv)

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world. Often, transparency of a given system is achieved by providing explanations of the behavior and predictions of the given system. Counterfactual explanations are a prominent instance of particular intuitive explanations of decision making systems. While a lot of different methods for computing counterfactual explanations exist, only very few work (apart from work from the causality domain) considers feature dependencies as well as plausibility which might limit the set of possible counterfactual explanations. In this work we enhance our previous work on convex modeling for computing counterfactual explanations by a mechanism for ensuring actionability and plausibility of the resulting counterfactual explanations.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

12 of 28 references.

References · 12

  1. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  2. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  3. 2018Actionable Recourse in Linear Classificationcited by 328
  4. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  5. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 144
  6. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  7. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  8. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  9. 2018A Simple and Effective Model-Based Variable Importance Measurecited by 51
  10. 2019On the computation of counterfactual explanations -- A surveycited by 33
  11. 2020Convex Density Constraints for Computing Plausible Counterfactual Explanationscited by 32
  12. 2020Latent-CF: A Simple Baseline for Reverse Counterfactual Explanationscited by 11

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Cited by year

Citing papers per year: 2022: 4; 2023: 0; 2024: 0; 2025: 2.

6 papers.

  1. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  2. 2022Explaining Reject Options of Learning Vector Quantization Classifierscited by 7
  3. 2022Model Agnostic Local Explanations of Rejectcited by 7
  4. 2022Counterfactual Explanations for Support Vector Machine Modelscited by 0
  5. 2025Interpretable Event Diagnosis in Water Distribution Networkscited by 0
  6. 2025RealAC: A Domain-Agnostic Framework for Realistic and Actionable Counterfactual Explanationscited by 0