# Counterfactual Fairness Is Not Demographic Parity, and Other Observations

arXiv: https://arxiv.org/abs/2402.02663 · HTML: https://mlsanity.com/paper/2402.02663/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [PC-Fairness: A Unified Framework for Measuring Causality-based Fairness](https://mlsanity.com/paper/1910.12586/) (2019)
- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [Stochastic Causal Programming for Bounding Treatment Effects](https://mlsanity.com/paper/2202.10806/) (2022)
- [Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness](https://mlsanity.com/paper/2310.19691/) (2023)
- [Selection, Ignorability and Challenges With Causal Fairness](https://mlsanity.com/paper/2202.13774/) (2022)
- [Counterfactual Fairness Is Basically Demographic Parity](https://mlsanity.com/paper/2208.03843/) (2022)
- [Pragmatic Fairness: Developing Policies with Outcome Disparity Control](https://mlsanity.com/paper/2301.12278/) (2023)

## Built on by (most cited first, 4 of 4)

- [Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing](https://mlsanity.com/paper/2501.06366/) (2025)
- [Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective](https://mlsanity.com/paper/2501.14710/) (2025)
- [Test-Time Fairness and Robustness in Large Language Models](https://mlsanity.com/paper/2406.07685/) (2024)
- [Counterfactually Fair Regression via Optimal Transport](https://mlsanity.com/paper/2605.28251/) (2026)

## Abstract (arXiv)

Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care. In this short note, I examine a recent claim that counterfactual fairness is equivalent to demographic parity. The claim fails to hold up upon closer examination. I will take the opportunity to address some broader misunderstandings about counterfactual fairness.