ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2018
Fair Inference on Outcomes.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Causal Inference in Health Disparities Research.Annual review of public health · 2026Review
- Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson's disease.PloS one · 2026Article
- A pipeline for enabling path-specific causal fairness in observational health data.Proceedings of the ... Conference on Fairness, Accountability, and Transparency · 2026Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- The Target Study: A Conceptual Model and Framework for Measuring Disparity.Sociological methods & research · 2025Article
- Reporting of Fairness Metrics in Clinical Risk Prediction Models Used for Precision Health: Scoping Review.Online journal of public health informatics · 2025Review
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Systematic decision frameworks for the socially responsible use of precision medicine.NPJ genomic medicine · 2024Article
- Causal fairness assessment of treatment allocation with electronic health records.Journal of biomedical informatics · 2024Article
- Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors.Advances in neural information processing systems · 2023Article
- Use of race in clinical algorithms.Science advances · 2023Article
- A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability.Entropy (Basel, Switzerland) · 2023Review
- Counterfactual fairness: The case study of a food delivery platform's reputational-ranking algorithm.Frontiers in psychology · 2022Article
- Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes.Entropy (Basel, Switzerland) · 2021Article
- Learning Optimal Fair Policies.Proceedings of machine learning research · 2019Article
- A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.Proceedings of machine learning research · 2019Article
- All Models are Wrong, butJournal of machine learning research : JMLR · 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
2 authors.
Funding
Abstract
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.
Identifiers
29796336PMC5963284What 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.