# Causal Inference for Social Discrimination Reasoning

arXiv: https://arxiv.org/abs/1608.03735 · HTML: https://mlsanity.com/paper/1608.03735/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2016.

## Built on by (most cited first, 8 of 8)

- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference](https://mlsanity.com/paper/2302.11944/) (2023)
- [Causal Fairness Assessment of Treatment Allocation with Electronic Health Records](https://mlsanity.com/paper/2211.11183/) (2022)
- [Beyond traditional assumptions in fair machine learning](https://mlsanity.com/paper/2101.12476/) (2021)
- [Mutatis Mutandis: Revisiting the Comparator in Discrimination Testing](https://mlsanity.com/paper/2405.13693/) (2024)
- [Counterfactual Situation Testing: From Single to Multidimensional Discrimination](https://mlsanity.com/paper/2502.01267/) (2025)
- [Causal Fairness-Guided Dataset Reweighting using Neural Networks](https://mlsanity.com/paper/2311.10512/) (2023)

## Abstract (arXiv)

The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.