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Fair Inference On Outcomes

arXiv 1705.10378 ↗First posted 29 May 2017stat.MLcited by 144

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Abstract (arXiv)

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl, 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

Key facts. Cited by 144 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 May 2017.

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Citing papers per year: 2021: 25; 2022: 38; 2023: 34; 2024: 21; 2025: 15; 2026: 11.

144 papers.

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  1. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Taskscited by 246
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  4. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  5. 2023CLadder: Assessing Causal Reasoning in Language Modelscited by 107
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  8. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  9. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  10. 2021Agent Incentives: A Causal Perspectivecited by 51
  11. 2021Sample Selection for Fair and Robust Trainingcited by 48
  12. 2022Causal Conceptions of Fairness and their Consequencescited by 38
38 more
  1. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
  2. 2022Causal Explanations and XAIcited by 30
  3. 2022Causally Testing Gender Bias in LLMs: A Case Study on Occupational Biascited by 28
  4. 2021Transport-based Counterfactual Modelscited by 27
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  7. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  8. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 21
  9. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 21
  10. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  11. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  12. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  13. 2023Reasoning about Causality in Gamescited by 20
  14. 2024Auditing the Use of Language Models to Guide Hiring Decisionscited by 20
  15. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 19
  16. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  17. 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
  18. 2021Removing biased data to improve fairness and accuracycited by 17
  19. 2021Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decompositioncited by 17
  20. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  21. 2023Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairnesscited by 17
  22. 2021Algorithmic Fairness Verification with Graphical Modelscited by 16
  23. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  24. 2022Causal Discovery for Fairnesscited by 16
  25. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  26. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  27. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
  28. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  29. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  30. 2023Robustness Implies Fairness in Causal Algorithmic Recoursecited by 12
  31. 2021Learning to be Fair: A Consequentialist Approach to Equitable Decision-Makingcited by 11
  32. 2022Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothingcited by 11
  33. 2023Fair Off-Policy Learning from Observational Datacited by 11
  34. 2022Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairnesscited by 10
  35. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  36. 2022Learning Counterfactually Invariant Predictorscited by 10
  37. 2022Fairness and robustness in anti-causal predictioncited by 10
  38. 2022DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learningcited by 10

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