ArticleProceedings of machine learning research2019
Learning Optimal Fair Policies.
Article in Proceedings of machine learning research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Race-conscious admissions algorithms and the law.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A pipeline for enabling path-specific causal fairness in observational health data.Proceedings of the ... Conference on Fairness, Accountability, and Transparency · 2026Article
- A Review of the Causal Decomposition Framework for Modeling Interventions that Reduce Disparities.Current epidemiology reports · 2025Article
- FRAMM: Fair ranking with missing modalities for clinical trial site selection.Patterns (New York, N.Y.) · 2024Article
- FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Causal Inference in the Presence of Interference in Sponsored Search Advertising.Frontiers in big data · 2022Article
- A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.Proceedings of machine learning research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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 & 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.
Identifiers
31886463PMC6935348What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.