# Fair Data Adaptation with Quantile Preservation

arXiv: https://arxiv.org/abs/1911.06685 · HTML: https://mlsanity.com/paper/1911.06685/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

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

- [Transport-based Counterfactual Models](https://mlsanity.com/paper/2108.13025/) (2021)
- [Conformalized Fairness via Quantile Regression](https://mlsanity.com/paper/2210.02015/) (2022)
- [fairadapt: Causal Reasoning for Fair Data Pre-processing](https://mlsanity.com/paper/2110.10200/) (2021)
- [Fairness in Multi-Task Learning via Wasserstein Barycenters](https://mlsanity.com/paper/2306.10155/) (2023)
- [OxonFair: A Flexible Toolkit for Algorithmic Fairness](https://mlsanity.com/paper/2407.13710/) (2024)
- [Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness](https://mlsanity.com/paper/2202.10816/) (2022)
- [Fairness-Accuracy Trade-Offs: A Causal Perspective](https://mlsanity.com/paper/2405.15443/) (2024)
- [fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation](https://mlsanity.com/paper/2104.00507/) (2021)
- [Bias-inducing geometries: an exactly solvable data model with fairness implications](https://mlsanity.com/paper/2205.15935/) (2022)
- [Causal Fairness for Outcome Control](https://mlsanity.com/paper/2306.05066/) (2023)
- [Reconciling Predictive and Statistical Parity: A Causal Approach](https://mlsanity.com/paper/2306.05059/) (2023)
- [What Is Fairness? On the Role of Protected Attributes and Fictitious Worlds](https://mlsanity.com/paper/2205.09622/) (2022)
- [Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective](https://mlsanity.com/paper/2501.14710/) (2025)
- [Causal Fair Machine Learning via Rank-Preserving Interventional Distributions](https://mlsanity.com/paper/2307.12797/) (2023)
- [Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness](https://mlsanity.com/paper/2408.03425/) (2024)
- [Counterfactual Situation Testing: From Single to Multidimensional Discrimination](https://mlsanity.com/paper/2502.01267/) (2025)
- [On Optimal Steering to Achieve Exact Fairness](https://mlsanity.com/paper/2509.15759/) (2025)
- [Fair Clustering: A Causal Perspective](https://mlsanity.com/paper/2312.09061/) (2023)
- [Privilege Scores](https://mlsanity.com/paper/2502.01211/) (2025)
- [Discrimination-free Insurance Pricing with Privatized Sensitive Attributes](https://mlsanity.com/paper/2504.11775/) (2025)
- [Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making](https://mlsanity.com/paper/2405.15446/) (2024)
- [Optimal Transport on Categorical Data for Counterfactuals using Compositional Data and Dirichlet Transport](https://mlsanity.com/paper/2501.15549/) (2025)
- [EquiPy: Sequential Fairness using Optimal Transport in Python](https://mlsanity.com/paper/2503.09866/) (2025)
- [What is a good matching of probability measures? A counterfactual lens on transport maps](https://mlsanity.com/paper/2509.16027/) (2025)
- [flowengineR: A Modular and Extensible Framework for Fair and Reproducible Workflow Design in R](https://mlsanity.com/paper/2511.00079/) (2025)
- [Fair regression under localized demographic parity constraints](https://mlsanity.com/paper/2603.25224/) (2026)
- [Tuning Derivatives for Causal Fairness in Machine Learning](https://mlsanity.com/paper/2605.05882/) (2026)
- [Causal Bias Detection in Generative Artificial Intelligence](https://mlsanity.com/paper/2605.11365/) (2026)
- [Controlling for Omitted Variable Bias in Deep Neural Networks](https://mlsanity.com/paper/2608.25930/) (2026)
- [Causally Fair Generation with Large Language Models](https://mlsanity.com/paper/2610.04444/) (2026)

## Abstract (arXiv)

Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enforced for a given classifier or, alternatively, the data can be adapated to ensure that every classifier trained on the data will adhere to desired fairness criteria. We present a practical data adaption method based on quantile preservation in causal structural equation models. The data adaptation is based on a presumed counterfactual model for the data. While the counterfactual model itself cannot be verified experimentally, we show that certain population notions of fairness are still guaranteed even if the counterfactual model is misspecified. The precise nature of the fulfilled non-causal fairness notion (such as demographic parity, separation or sufficiency) depends on the structure of the underlying causal model and the choice of resolving variables. We describe an implementation of the proposed data adaptation procedure based on Random Forests and demonstrate its practical use on simulated and real-world data.