mlsanityData to 14 Oct 2026

Path-Specific Counterfactual Fairness

arXiv 1802.08139 ↗First posted 22 Feb 2018stat.MLcited by 181

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Abstract (arXiv)

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects observations adversely affected by the sensitive attribute, and uses these to form a decision. This avoids disregarding fair information, and does not require an often intractable computation of the path-specific effect. We leverage recent developments in deep learning and approximate inference to achieve a solution that is widely applicable to complex, non-linear scenarios.

Key facts. Cited by 181 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Feb 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 24; 2022: 39; 2023: 37; 2024: 33; 2025: 21; 2026: 27.

181 papers.

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  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  2. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 148
  3. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  4. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  5. 2021Trustworthy AI: A Computational Perspectivecited by 93
  6. 2023Fast Model Debias with Machine Unlearningcited by 72
  7. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  8. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  9. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  10. 2023TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Modelscited by 52
  11. 2023Causal normalizing flows: from theory to practicecited by 51
  12. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
38 more
  1. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  2. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
  3. 2022Causal Explanations and XAIcited by 30
  4. 2022Counterfactual harmcited by 29
  5. 2021Transport-based Counterfactual Modelscited by 27
  6. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  7. 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
  8. 2022Fairness and Explainability: Bridging the Gap Towards Fair Model Explanationscited by 22
  9. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 21
  10. 2022Learning fair representation with a parametric integral probability metriccited by 20
  11. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  12. 2024Auditing the Use of Language Models to Guide Hiring Decisionscited by 20
  13. 2021Deep Clustering based Fair Outlier Detectioncited by 19
  14. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 19
  15. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  16. 2022D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Biascited by 18
  17. 2021Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decompositioncited by 17
  18. 2023Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairnesscited by 17
  19. 2021Robust Semantic Interpretability: Revisiting Concept Activation Vectorscited by 16
  20. 2021Deconfounded Visual Groundingcited by 16
  21. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  22. 2022Causal Discovery for Fairnesscited by 16
  23. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  24. 2021Achieving Counterfactual Fairness for Causal Banditcited by 15
  25. 2022The Fairness of Credit Scoring Modelscited by 15
  26. 2022Counterfactual Fairness Is Basically Demographic Paritycited by 15
  27. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  28. 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
  29. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  30. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  31. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  32. 2023No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imagingcited by 11
  33. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
  34. 2025CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglementcited by 11
  35. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  36. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  37. 2022Learning Counterfactually Invariant Predictorscited by 10
  38. 2022Fairness and robustness in anti-causal predictioncited by 10

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