# Causal Conceptions of Fairness and their Consequences

arXiv: https://arxiv.org/abs/2207.05302 · HTML: https://mlsanity.com/paper/2207.05302/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 73 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 27 of 27)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [PC-Fairness: A Unified Framework for Measuring Causality-based Fairness](https://mlsanity.com/paper/1910.12586/) (2019)
- [From Parity to Preference-based Notions of Fairness in Classification](https://mlsanity.com/paper/1707.00010/) (2017)
- [Counterfactual Risk Assessments, Evaluation, and Fairness](https://mlsanity.com/paper/1909.00066/) (2019)
- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds](https://mlsanity.com/paper/2009.02841/) (2020)
- [What's Sex Got To Do With Fair Machine Learning?](https://mlsanity.com/paper/2006.01770/) (2020)
- [Causal Feature Selection for Algorithmic Fairness](https://mlsanity.com/paper/2006.06053/) (2020)
- [Principal Fairness for Human and Algorithmic Decision-Making](https://mlsanity.com/paper/2005.10400/) (2020)
- [Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment](https://mlsanity.com/paper/2012.02845/) (2020)
- [Fair Allocation through Selective Information Acquisition](https://mlsanity.com/paper/1911.02715/) (2019)
- [Causal Multi-Level Fairness](https://mlsanity.com/paper/2010.07343/) (2020)
- [Equal Opportunity and Affirmative Action via Counterfactual Predictions](https://mlsanity.com/paper/1905.10870/) (2019)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [A Causal Framework for Observational Studies of Discrimination](https://mlsanity.com/paper/2006.12460/) (2020)

## Built on by (most cited first, 30 of 38)

- [LEACE: Perfect linear concept erasure in closed form](https://mlsanity.com/paper/2306.03819/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [The Essential Role of Causality in Foundation World Models for Embodied AI](https://mlsanity.com/paper/2402.06665/) (2024)
- [Zero-shot causal learning](https://mlsanity.com/paper/2301.12292/) (2023)
- [Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness](https://mlsanity.com/paper/2310.19691/) (2023)
- [Counterfactual Fairness Is Basically Demographic Parity](https://mlsanity.com/paper/2208.03843/) (2022)
- [Fair Off-Policy Learning from Observational Data](https://mlsanity.com/paper/2303.08516/) (2023)
- [Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework](https://mlsanity.com/paper/2311.18460/) (2023)
- [Fairness-Accuracy Trade-Offs: A Causal Perspective](https://mlsanity.com/paper/2405.15443/) (2024)
- [Causal Fairness for Outcome Control](https://mlsanity.com/paper/2306.05066/) (2023)
- [Counterfactual Fairness by Combining Factual and Counterfactual Predictions](https://mlsanity.com/paper/2409.01977/) (2024)
- [Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors](https://mlsanity.com/paper/2301.08987/) (2023)
- [Are demographically invariant models and representations in medical imaging fair?](https://mlsanity.com/paper/2305.01397/) (2023)
- [Auditing Fairness under Unobserved Confounding](https://mlsanity.com/paper/2403.14713/) (2024)
- [What is Fair? Defining Fairness in Machine Learning for Health](https://mlsanity.com/paper/2406.09307/) (2024)
- [Fairness in Forecasting of Observations of Linear Dynamical Systems](https://mlsanity.com/paper/2209.05274/) (2022)
- [Group-blind optimal transport to group parity and its constrained variants](https://mlsanity.com/paper/2310.11407/) (2023)
- [Improving Fairness with Ensemble Combination: Margin-Dependent Bounds](https://mlsanity.com/paper/2301.10813/) (2023)
- [Standardized Interpretable Fairness Measures for Continuous Risk Scores](https://mlsanity.com/paper/2308.11375/) (2023)
- [Procedural Fairness Through Decoupling Objectionable Data Generating Components](https://mlsanity.com/paper/2311.14688/) (2023)
- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [Measuring Model-Induced Discrimination via Efficient Fairness Approximation](https://mlsanity.com/paper/2405.09251/) (2024)
- [Formalising Anti-Discrimination Law in Automated Decision Systems](https://mlsanity.com/paper/2407.00400/) (2024)
- [Modeling Discrimination with Causal Abstraction](https://mlsanity.com/paper/2501.08429/) (2025)
- [Path-specific effects for pulse-oximetry guided decisions in critical care](https://mlsanity.com/paper/2506.12371/) (2025)
- [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022)
- [Fair Clustering: A Causal Perspective](https://mlsanity.com/paper/2312.09061/) (2023)
- [A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns](https://mlsanity.com/paper/2409.13210/) (2024)
- [A Systems Thinking Approach to Algorithmic Fairness](https://mlsanity.com/paper/2412.16641/) (2024)

## Abstract (arXiv)

Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness relate to one another, or what the consequences are of using these definitions as design principles. Here, we first assemble and categorize popular causal definitions of algorithmic fairness into two broad families: (1) those that constrain the effects of decisions on counterfactual disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions almost always -- in a measure theoretic sense -- result in strongly Pareto dominated decision policies, meaning there is an alternative, unconstrained policy favored by every stakeholder with preferences drawn from a large, natural class. For example, in the case of college admissions decisions, policies constrained to satisfy causal fairness definitions would be disfavored by every stakeholder with neutral or positive preferences for both academic preparedness and diversity. Indeed, under a prominent definition of causal fairness, we prove the resulting policies require admitting all students with the same probability, regardless of academic qualifications or group membership. Our results highlight formal limitations and potential adverse consequences of common mathematical notions of causal fairness.