mlsanityData to 7 Oct 2026

Learning Models for Actionable Recourse

arXiv 2011.06146 ↗First posted 12 Nov 2020cs.LG cs.AIcited by 18

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Abstract (arXiv)

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant denied a loan) with recourse -- i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability without sacrificing accuracy. We demonstrate the efficacy of our approach via extensive experiments on real data.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 5; 2024: 3; 2025: 2; 2026: 2.

18 papers.

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  1. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  2. 2022Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysiscited by 38
  3. 2022Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measurecited by 33
  4. 2021CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanationscited by 17
  5. 2023On the Actionability of Outcome Predictioncited by 9
  6. 2024Learning Decision Trees and Forests with Algorithmic Recoursecited by 7
  7. 2022On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspectivecited by 6
  8. 2023Algorithmic Censoring in Dynamic Learning Systemscited by 5
  9. 2024Sparse and Faithful Explanations Without Sparse Modelscited by 5
  10. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  11. 2023Prediction without Preclusion: Recourse Verification with Reachable Setscited by 4
  12. 2022Counterfactual Explanations for Natural Language Interfacescited by 1
6 more
  1. 2023Personalized Path Recourse for Reinforcement Learning Agentscited by 0
  2. 2024Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitationcited by 0
  3. 2025Responsiveness Verification: Will Predictions Change? How Much? How Often?cited by 0
  4. 2025Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairnesscited by 0
  5. 2026Counterfactual Training: Teaching Models Plausible and Actionable Explanationscited by 0
  6. 2026Algorithmic Recourse of In-Context Learning for Tabular Datacited by 0