mlsanityData to 14 Oct 2026

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

arXiv 1910.12586 ↗First posted 20 Oct 2019cs.LG cs.AI cs.CYcited by 67

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Abstract (arXiv)

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Oct 2019.

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Citing papers per year: 2021: 10; 2022: 18; 2023: 15; 2024: 11; 2025: 5; 2026: 8.

67 papers.

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  1. 2021Fairness via Representation Neutralizationcited by 63
  2. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  3. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  4. 2022Causal Explanations and XAIcited by 30
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25
  7. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  8. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  9. 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
  10. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  11. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  12. 2023Causal Deep Learningcited by 20
38 more
  1. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  2. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  3. 2021Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decompositioncited by 17
  4. 2023Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairnesscited by 17
  5. 2021Achieving Counterfactual Fairness for Causal Banditcited by 15
  6. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  7. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  8. 2021Learning to be Fair: A Consequentialist Approach to Equitable Decision-Makingcited by 11
  9. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  10. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  11. 2022Fairness-aware Model-agnostic Positive and Unlabeled Learningcited by 10
  12. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  13. 2024Fairness-Accuracy Trade-Offs: A Causal Perspectivecited by 10
  14. 2023Causal Fairness for Outcome Controlcited by 8
  15. 2023Towards Counterfactual Fairness-aware Domain Generalization in Changing Environmentscited by 8
  16. 2023Towards Bounding Causal Effects under Markov Equivalencecited by 8
  17. 2023Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factorscited by 7
  18. 2023Reconciling Predictive and Statistical Parity: A Causal Approachcited by 7
  19. 2024Policy Learning for Balancing Short-Term and Long-Term Rewardscited by 7
  20. 2022On Learning and Testing of Counterfactual Fairness through Data Preprocessingcited by 6
  21. 2022COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendationcited by 6
  22. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  23. 2026Bi-directional Bias Attribution: Debiasing Large Language Models without Modifying Promptscited by 5
  24. 2024Counterfactual Fairness Is Not Demographic Parity, and Other Observationscited by 4
  25. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  26. 2022The Fairness Field Guide: Perspectives from Social and Formal Sciencescited by 3
  27. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  28. 2024A New Paradigm for Counterfactual Reasoning in Fairness and Recoursecited by 3
  29. 2024What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learningcited by 3
  30. 2021Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effectscited by 2
  31. 2022SCALES: From Fairness Principles to Constrained Decision-Makingcited by 2
  32. 2023Designing Equitable Algorithmscited by 2
  33. 2024Formalising Anti-Discrimination Law in Automated Decision Systemscited by 2
  34. 2024Towards counterfactual fairness through auxiliary variablescited by 2
  35. 2025CodeSCM: Causal Analysis for Multi-Modal Code Generationcited by 2
  36. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  37. 2025FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agentscited by 2
  38. 2021A Causal Approach for Unfair Edge Prioritization and Discrimination Removalcited by 1

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