mlsanityData to 7 Oct 2026

Causal Modeling for Fairness in Dynamical Systems

arXiv 1909.09141 ↗First posted 18 Sep 2019cs.LG cs.AI cs.CYcited by 46

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Abstract (arXiv)

In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in such dynamical systems. We show that this formulation affords several new directions of inquiry to the modeler, where causal assumptions can be expressed and manipulated. We emphasize the importance of computing interventional quantities in the dynamical fairness setting, and show how causal assumptions enable simulation (when environment dynamics are known) and off-policy estimation (when dynamics are unknown) of intervention on short- and long-term outcomes, at both the group and individual levels.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 14; 2023: 10; 2024: 6; 2025: 2; 2026: 6.

46 papers.

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  1. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  2. 2022MoCoDA: Model-based Counterfactual Data Augmentationcited by 44
  3. 2022Fairness Transferability Subject to Bounded Distribution Shiftcited by 28
  4. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  5. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  6. 2024Supervised Algorithmic Fairness in Distribution Shifts: A Surveycited by 23
  7. 2023Causal Reinforcement Learning: A Surveycited by 22
  8. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 20
  9. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
  10. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  11. 2021Directive Explanations for Actionable Explainability in Machine Learning Applicationscited by 14
  12. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
34 more
  1. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  2. 2022Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systemscited by 13
  3. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  4. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  5. 2021Fairness with Continuous Optimal Transportcited by 9
  6. 2022Reinforcement Learning with Stepwise Fairness Constraintscited by 9
  7. 2023Designing Long-term Group Fair Policies in Dynamical Systemscited by 8
  8. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  9. 2023Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factorscited by 7
  10. 2023Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneouslycited by 7
  11. 2021Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environmentcited by 6
  12. 2022Algorithmic decision making methods for fair credit scoringcited by 5
  13. 2021Statistical discrimination in learning agentscited by 4
  14. 2024Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learningcited by 4
  15. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
  16. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  17. 2024What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learningcited by 3
  18. 2022Causal Structure Learning with Recommendation Systemcited by 2
  19. 2026Fairness under uncertainty in sequential decisionscited by 2
  20. 2021Fairness Through Counterfactual Utilitiescited by 1
  21. 2023Toward A Logical Theory Of Fairness and Biascited by 1
  22. 2025When Counterfactual Reasoning Fails: Chaos and Real-World Complexitycited by 1
  23. 2022Achievement and Fragility of Long-term Equitabilitycited by 0
  24. 2022Tensions Between the Proxies of Human Values in AIcited by 0
  25. 2023FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systemscited by 0
  26. 2023Fairness in KI-Systemencited by 0
  27. 2023FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-Systemcited by 0
  28. 2024Fair Resource Allocation in Weakly Coupled Markov Decision Processescited by 0
  29. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  30. 2026Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisionscited by 0
  31. 2026The Design and Composition of Structural Causal Decision Processescited by 0
  32. 2026Causal Fairness for Survival Analysiscited by 0
  33. 2026Long-term Fairness with Selective Labelscited by 0
  34. 2026A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learningcited by 0