# Impartial Predictive Modeling and the Use of Proxy Variables

arXiv: https://arxiv.org/abs/1608.00528 · HTML: https://mlsanity.com/paper/1608.00528/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 5 of 5)

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- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Neural Spatiotemporal Point Processes: Trends and Challenges](https://mlsanity.com/paper/2502.09341/) (2025)
- [Fairness in Machine Learning-based Hand Load Estimation: A Case Study on Load Carriage Tasks](https://mlsanity.com/paper/2504.05610/) (2025)
- [Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency](https://mlsanity.com/paper/2402.15561/) (2024)

## Abstract (arXiv)

Fairness aware data mining (FADM) aims to prevent algorithms from discriminating against protected groups. The literature has come to an impasse as to what constitutes explainable variability as opposed to discrimination. This distinction hinges on a rigorous understanding of the role of proxy variables; i.e., those variables which are associated both the protected feature and the outcome of interest. We demonstrate that fairness is achieved by ensuring impartiality with respect to sensitive characteristics and provide a framework for impartiality by accounting for different perspectives on the data generating process. In particular, fairness can only be precisely defined in a full-data scenario in which all covariates are observed. We then analyze how these models may be conservatively estimated via regression in partial-data settings. Decomposing the regression estimates provides insights into previously unexplored distinctions between explainable variability and discrimination that illuminate the use of proxy variables in fairness aware data mining.