mlsanityData to 7 Oct 2026

Learning Optimal Fair Policies

arXiv 1809.02244 ↗First posted 6 Sep 2018cs.LG stat.MLcited by 49

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Abstract (arXiv)

Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or unfairness in our society. In this paper, we consider how to make optimal but fair decisions, which "break the cycle of injustice" by correcting for the unfair dependence of both decisions and outcomes on sensitive features (e.g., variables that correspond to gender, race, disability, or other protected attributes). We use methods from causal inference and constrained optimization to learn optimal policies in a way that addresses multiple potential biases which afflict data analysis in sensitive contexts, extending the approach of (Nabi and Shpitser 2018). Our proposal comes equipped with the theoretical guarantee that the chosen fair policy will induce a joint distribution for new instances that satisfies given fairness constraints. We illustrate our approach with both synthetic data and real criminal justice data.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 11; 2023: 13; 2024: 7; 2025: 4; 2026: 6.

49 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Agent Incentives: A Causal Perspectivecited by 51
  3. 2021Selective Regression Under Fairness Criteriacited by 34
  4. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  5. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  6. 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
  7. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 20
  8. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  9. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  10. 2022Omnipredictors for Constrained Optimizationcited by 14
  11. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  12. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
37 more
  1. 2022Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothingcited by 11
  2. 2023Fair Off-Policy Learning from Observational Datacited by 11
  3. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  4. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  5. 2024Fairness in Reinforcement Learning: A Surveycited by 9
  6. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  7. 2021End-To-End Bias Mitigation: Removing Gender Bias in Deep Learningcited by 6
  8. 2022What Is Fairness? On the Role of Protected Attributes and Fictitious Worldscited by 6
  9. 2023FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selectioncited by 6
  10. 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learningcited by 6
  11. 2021Disaggregated Interventions to Reduce Inequalitycited by 5
  12. 2022Counterfactuals for the Futurecited by 5
  13. 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledgecited by 4
  14. 2023Causal Fair Machine Learning via Rank-Preserving Interventional Distributionscited by 4
  15. 2023Group-blind optimal transport to group parity and its constrained variantscited by 4
  16. 2024Uplift modeling with continuous treatments: A predict-then-optimize approachcited by 4
  17. 2022RISE: Robust Individualized Decision Learning with Sensitive Variablescited by 3
  18. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  19. 2024What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learningcited by 3
  20. 2025FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generationcited by 3
  21. 2022SCALES: From Fairness Principles to Constrained Decision-Makingcited by 2
  22. 2023FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantationcited by 2
  23. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  24. 2021Towards Fairness Certification in Artificial Intelligencecited by 1
  25. 2023Pragmatic Fairness: Developing Policies with Outcome Disparity Controlcited by 1
  26. 2024Fairness in Reinforcement Learning with Bisimulation Metricscited by 1
  27. 2025Privilege Scorescited by 1
  28. 2021Identifying Best Fair Interventioncited by 0
  29. 2024Fairness Evaluation for Uplift Modeling in the Absence of Ground Truthcited by 0
  30. 2024Fair Risk Minimization under Causal Path-Specific Effect Constraintscited by 0
  31. 2025Assessing Racial Disparities in Healthcare Expenditures via Mediator Distribution Shiftscited by 0
  32. 2025Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinantscited by 0
  33. 2026A pipeline for enabling path-specific causal fairness in observational health datacited by 0
  34. 2026Masking Causality and Conditional Dependencecited by 0
  35. 2026Causal Fairness for Survival Analysiscited by 0
  36. 2026Measuring and Mitigating Bias in Code Generated by Large Language Modelscited by 0
  37. 2026Proximal Mediation Analysis with Hidden Recanting Witnessescited by 0