# Fair Clustering: A Causal Perspective

arXiv: https://arxiv.org/abs/2312.09061 · HTML: https://mlsanity.com/paper/2312.09061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 29 of 29)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [PC-Fairness: A Unified Framework for Measuring Causality-based Fairness](https://mlsanity.com/paper/1910.12586/) (2019)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Individual Fairness for k-Clustering](https://mlsanity.com/paper/2002.06742/) (2020)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [On the cost of essentially fair clusterings](https://mlsanity.com/paper/1811.10319/) (2018)
- [Fair clustering via equitable group representations](https://mlsanity.com/paper/2006.11009/) (2020)
- [Causal Fairness Analysis](https://mlsanity.com/paper/2207.11385/) (2022)
- [Causal Conceptions of Fairness and their Consequences](https://mlsanity.com/paper/2207.05302/) (2022)
- [Improved Approximation Algorithms for Individually Fair Clustering](https://mlsanity.com/paper/2106.14043/) (2021)
- [Fair Data Adaptation with Quantile Preservation](https://mlsanity.com/paper/1911.06685/) (2019)
- [Better Algorithms for Individually Fair k-Clustering](https://mlsanity.com/paper/2106.12150/) (2021)
- [A Center in Your Neighborhood: Fairness in Facility Location](https://mlsanity.com/paper/1908.09041/) (2019)
- [Approximation Algorithms for Socially Fair Clustering](https://mlsanity.com/paper/2103.02512/) (2021)
- [A Notion of Individual Fairness for Clustering](https://mlsanity.com/paper/2006.04960/) (2020)
- [Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints](https://mlsanity.com/paper/2103.02013/) (2021)
- [fairadapt: Causal Reasoning for Fair Data Pre-processing](https://mlsanity.com/paper/2110.10200/) (2021)
- [Distributional Individual Fairness in Clustering](https://mlsanity.com/paper/2006.12589/) (2020)
- [Tight FPT Approximation for Socially Fair Clustering](https://mlsanity.com/paper/2106.06755/) (2021)

## Built on by (most cited first, 1 of 1)

- [A pipeline for enabling path-specific causal fairness in observational health data](https://mlsanity.com/paper/2601.09841/) (2026)

## Abstract (arXiv)

Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods rely on notions of fairness that do not capture any information on the underlying causal mechanisms. We show that optimising for non-causal fairness notions can paradoxically induce direct discriminatory effects from a causal standpoint. We present a clustering approach that incorporates causal fairness metrics to provide a more nuanced approach to fairness in unsupervised learning. Our approach enables the specification of the causal fairness metrics that should be minimised. We demonstrate the efficacy of our methodology using datasets known to harbour unfair biases.