# Causal Discovery for Fairness

arXiv: https://arxiv.org/abs/2206.06685 · HTML: https://mlsanity.com/paper/2206.06685/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 76 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [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)
- [Survey on Causal-based Machine Learning Fairness Notions](https://mlsanity.com/paper/2010.09553/) (2020)
- [Causal Discovery Toolbox: Uncover causal relationships in Python](https://mlsanity.com/paper/1903.02278/) (2019)
- [A fast PC algorithm for high dimensional causal discovery with multi-core PCs](https://mlsanity.com/paper/1502.02454/) (2015)
- [Achieving non-discrimination in data release](https://mlsanity.com/paper/1611.07438/) (2016)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Machine learning fairness notions: Bridging the gap with real-world applications](https://mlsanity.com/paper/2006.16745/) (2020)
- [On Discrimination Discovery and Removal in Ranked Data using Causal Graph](https://mlsanity.com/paper/1803.01901/) (2018)
- [Fairness through Equality of Effort](https://mlsanity.com/paper/1911.08292/) (2019)
- [The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks](https://mlsanity.com/paper/1806.06209/) (2018)
- [A Review on Algorithms for Constraint-based Causal Discovery](https://mlsanity.com/paper/1611.03977/) (2016)
- [Exposing the Probabilistic Causal Structure of Discrimination](https://mlsanity.com/paper/1510.00552/) (2015)
- [Identifiability of Causal-based Fairness Notions: A State of the Art](https://mlsanity.com/paper/2203.05900/) (2022)

## Built on by (most cited first, 16 of 16)

- [Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness](https://mlsanity.com/paper/2310.19691/) (2023)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [FairPFN: A Tabular Foundation Model for Causal Fairness](https://mlsanity.com/paper/2506.07049/) (2025)
- [Local Causal Discovery for Structural Evidence of Direct Discrimination](https://mlsanity.com/paper/2405.14848/) (2024)
- [Causal Discovery Under Local Privacy](https://mlsanity.com/paper/2311.04037/) (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)
- [Causal Fairness-Guided Dataset Reweighting using Neural Networks](https://mlsanity.com/paper/2311.10512/) (2023)
- [Mitigating Nonlinear Algorithmic Bias in Binary Classification](https://mlsanity.com/paper/2312.05429/) (2023)
- [IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark](https://mlsanity.com/paper/2405.16069/) (2024)
- [Counterfactual Fairness with Graph Uncertainty](https://mlsanity.com/paper/2601.03203/) (2026)
- [On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis](https://mlsanity.com/paper/2207.04053/) (2022)
- [What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem](https://mlsanity.com/paper/2305.09535/) (2023)
- [Dissecting Causal Biases](https://mlsanity.com/paper/2310.13364/) (2023)
- [Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models](https://mlsanity.com/paper/2506.18732/) (2025)
- [Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare](https://mlsanity.com/paper/2603.15926/) (2026)
- [GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits](https://mlsanity.com/paper/2608.02877/) (2026)

## Abstract (arXiv)

It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities. Identifying and measuring reliably fairness/discrimination is better achieved using causality which considers the causal relation, beyond mere association, between the sensitive attribute (e.g. gender, race, religion, etc.) and the decision (e.g. job hiring, loan granting, etc.). The big impediment to the use of causality to address fairness, however, is the unavailability of the causal model (typically represented as a causal graph). Existing causal approaches to fairness in the literature do not address this problem and assume that the causal model is available. In this paper, we do not make such assumption and we review the major algorithms to discover causal relations from observable data. This study focuses on causal discovery and its impact on fairness. In particular, we show how different causal discovery approaches may result in different causal models and, most importantly, how even slight differences between causal models can have significant impact on fairness/discrimination conclusions. These results are consolidated by empirical analysis using synthetic and standard fairness benchmark datasets. The main goal of this study is to highlight the importance of the causal discovery step to appropriately address fairness using causality.