# Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

arXiv: https://arxiv.org/abs/1903.11719 · HTML: https://mlsanity.com/paper/1903.11719/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 30 of 33)

- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Sample Selection for Fair and Robust Training](https://mlsanity.com/paper/2110.14222/) (2021)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [Counterfactual Fairness with Partially Known Causal Graph](https://mlsanity.com/paper/2205.13972/) (2022)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification](https://mlsanity.com/paper/2101.07361/) (2021)
- [Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework](https://mlsanity.com/paper/2311.18460/) (2023)
- [Counterfactual Fairness by Combining Factual and Counterfactual Predictions](https://mlsanity.com/paper/2409.01977/) (2024)
- [On Learning and Testing of Counterfactual Fairness through Data Preprocessing](https://mlsanity.com/paper/2202.12440/) (2022)
- [COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation](https://mlsanity.com/paper/2210.15500/) (2022)
- [Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach](https://mlsanity.com/paper/2401.10632/) (2024)
- [Fairness-Aware Estimation of Graphical Models](https://mlsanity.com/paper/2408.17396/) (2024)
- [Promises and Challenges of Causality for Ethical Machine Learning](https://mlsanity.com/paper/2201.10683/) (2022)
- [Locating disparities in machine learning](https://mlsanity.com/paper/2208.06680/) (2022)
- [The Fairness Field Guide: Perspectives from Social and Formal Sciences](https://mlsanity.com/paper/2201.05216/) (2022)
- [The price of unfairness in linear bandits with biased feedback](https://mlsanity.com/paper/2203.09784/) (2022)
- [Fair-CDA: Continuous and Directional Augmentation for Group Fairness](https://mlsanity.com/paper/2304.00295/) (2023)
- [Detecting and Mitigating Algorithmic Bias in Binary Classification using Causal Modeling](https://mlsanity.com/paper/2310.12421/) (2023)
- [Counterfactual Fairness through Transforming Data Orthogonal to Bias](https://mlsanity.com/paper/2403.17852/) (2024)
- [Lookahead Counterfactual Fairness](https://mlsanity.com/paper/2412.01065/) (2024)
- [Assessing Electricity Service Unfairness with Transfer Counterfactual Learning](https://mlsanity.com/paper/2310.03258/) (2023)
- [Mitigating Nonlinear Algorithmic Bias in Binary Classification](https://mlsanity.com/paper/2312.05429/) (2023)
- [Learning Fair Policies for Multi-stage Selection Problems from Observational Data](https://mlsanity.com/paper/2312.13173/) (2023)
- [Counterfactual Fairness in Mortgage Lending via Matching and Randomization](https://mlsanity.com/paper/2112.02170/) (2021)
- [Causal Intervention for Fairness in Multi-behavior Recommendation](https://mlsanity.com/paper/2209.04589/) (2022)
- [Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments](https://mlsanity.com/paper/2309.01780/) (2023)
- [Practical Guide for Causal Pathways and Sub-group Disparity Analysis](https://mlsanity.com/paper/2407.02702/) (2024)

## Abstract (arXiv)

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.