mlsanityData to 7 Oct 2026

RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

arXiv 2606.16113 ↗First posted 15 Jun 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable model decision. Despite rapid methodological progress, principled comparison remains elusive; existing frameworks are often difficult to extend and lack both interoperability and systematic verification that integrated methods faithfully reproduce their originally reported claims. We introduce RecourseBench, a unified evaluation framework built around three commitments: modularity, reproducibility, and interactivity. The framework decomposes the pipeline into five fully decoupled layers---Data, Preprocessing, Model, Recourse Method, and Evaluation---governed by abstract interfaces and a dynamic registry. Every integrated method is classified into a four-tier reproducibility taxonomy based on artifact availability, followed by a systematic verification of its core empirical claims. We further provide an interactive web interface for flexible, configuration-driven exploration across datasets, model architectures, methods, and evaluations. To our knowledge, RecourseBench is the first benchmark to explicitly ground recourse evaluation in structured claim verification and mathematically rigorous reproducibility standards, all while featuring the largest collection of state-of-the-art recourse algorithms (27 in total).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 62 references that are in the map. First posted on arXiv 15 Jun 2026.

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35 of 62 references.

References · 35

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  2. 2017Deep Reinforcement Learning that Matterscited by 727
  3. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  4. 2018Actionable Recourse in Linear Classificationcited by 327
  5. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
  6. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 232
  7. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  8. 2019Learning Model-Agnostic Counterfactual Explanations for Tabular Datacited by 146
  9. 2019Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifierscited by 143
  10. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 128
  11. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  12. 2020Getting a CLUE: A Method for Explaining Uncertainty Estimatescited by 92
23 more
  1. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  2. 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
  3. 2017Inverse Classification for Comparison-based Interpretability in Machine Learningcited by 49
  4. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  5. 2021Consistent Counterfactuals for Deep Modelscited by 37
  6. 2024Reproducibility in Machine Learning-based Research: Overview, Barriers and Driverscited by 34
  7. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
  8. 2023Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisationcited by 20
  9. 2022Robust Bayesian Recoursecited by 18
  10. 2023Promoting Counterfactual Robustness through Diversitycited by 17
  11. 2021Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learningcited by 15
  12. 2024What is Reproducibility in Artificial Intelligence and Machine Learning Research?cited by 11
  13. 2021Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functionscited by 10
  14. 2023Endogenous Macrodynamics in Algorithmic Recoursecited by 9
  15. 2024Counterfactual Explanations via Riemannian Latent Space Traversalcited by 9
  16. 2023Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbationscited by 6
  17. 2025RobustX: Robust Counterfactual Explanations Made Easycited by 6
  18. 2023Explaining Black-Box Models through Counterfactualscited by 4
  19. 2023Coverage-Validity-Aware Algorithmic Recoursecited by 4
  20. 2024Learning-Augmented Robust Algorithmic Recoursecited by 4
  21. 2024Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspectivecited by 3
  22. 2025QRA++: Quantified Reproducibility Assessment for Common Types of Results in Natural Language Processingcited by 1
  23. 2025Argumentative Ensembling for Robust Recourse under Model Multiplicitycited by 1

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