mlsanityData to 14 Oct 2026

Explaining Black-Box Models through Counterfactuals

arXiv 2308.07198 ↗First posted 14 Aug 2023cs.LG cs.AI cs.PLcited by 4

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Abstract (arXiv)

We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realistic and actionable changes can be used to provide AR: a set of proposed actions for individuals to change an undesirable outcome for the better. In this article, we discuss the usefulness of CE for Explainable Artificial Intelligence and demonstrate the functionality of our package. The package is straightforward to use and designed with a focus on customization and extensibility. We envision it to one day be the go-to place for explaining arbitrary predictive models in Julia through a diverse suite of counterfactual generators.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 14 Aug 2023.

Builds on

25 of 45 references.

References · 25

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  5. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  6. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  7. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  8. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 443
  9. 2018Actionable Recourse in Linear Classificationcited by 328
  10. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  11. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  12. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 140
13 more
  1. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 131
  2. 2020A survey of algorithmic recourse: definitions, formulations, solutions, and prospectscited by 118
  3. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  4. 2021Towards Robust and Reliable Algorithmic Recoursecited by 102
  5. 2020Getting a CLUE: A Method for Explaining Uncertainty Estimatescited by 92
  6. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 86
  7. 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
  8. 2017Inverse Classification for Comparison-based Interpretability in Machine Learningcited by 50
  9. 2021Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertaintiescited by 41
  10. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  11. 2021Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutionscited by 16
  12. 2020MLJ: A Julia package for composable machine learningcited by 9
  13. 2023counterfactuals: An R Package for Counterfactual Explanation Methodscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 3.

4 papers.

  1. 2023Faithful Model Explanations through Energy-Constrained Conformal Counterfactualscited by 5
  2. 2026From Features to Actions: Explainability in Traditional and Agentic AI Systemscited by 1
  3. 2026Counterfactual Training: Teaching Models Plausible and Actionable Explanationscited by 0
  4. 2026RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluationcited by 0