# Causal Feature Selection for Algorithmic Fairness

arXiv: https://arxiv.org/abs/2006.06053 · HTML: https://mlsanity.com/paper/2006.06053/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 25 of 25)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Causal Conceptions of Fairness and their Consequences](https://mlsanity.com/paper/2207.05302/) (2022)
- [Counterfactual Fairness with Partially Known Causal Graph](https://mlsanity.com/paper/2205.13972/) (2022)
- [Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness](https://mlsanity.com/paper/2310.19691/) (2023)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [NeuFair: Neural Network Fairness Repair with Dropout](https://mlsanity.com/paper/2407.04268/) (2024)
- [Learning Counterfactually Invariant Predictors](https://mlsanity.com/paper/2207.09768/) (2022)
- [Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition](https://mlsanity.com/paper/2307.11333/) (2023)
- [Mind the Graph When Balancing Data for Fairness or Robustness](https://mlsanity.com/paper/2406.17433/) (2024)
- [Counterfactual Fairness by Combining Factual and Counterfactual Predictions](https://mlsanity.com/paper/2409.01977/) (2024)
- [Consistent Range Approximation for Fair Predictive Modeling](https://mlsanity.com/paper/2212.10839/) (2022)
- [SoK: Unintended Interactions among Machine Learning Defenses and Risks](https://mlsanity.com/paper/2312.04542/) (2023)
- [Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach](https://mlsanity.com/paper/2401.10632/) (2024)
- [A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition](https://mlsanity.com/paper/2406.04562/) (2024)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Fair Causal Feature Selection](https://mlsanity.com/paper/2306.10336/) (2023)
- [Causal Feature Selection for Responsible Machine Learning](https://mlsanity.com/paper/2402.02696/) (2024)
- [Fairness-Aware Streaming Feature Selection with Causal Graphs](https://mlsanity.com/paper/2408.12665/) (2024)
- [CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness](https://mlsanity.com/paper/2509.15199/) (2025)
- [REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values](https://mlsanity.com/paper/2403.08880/) (2024)
- [Evaluating Fair Feature Selection in Machine Learning for Healthcare](https://mlsanity.com/paper/2403.19165/) (2024)
- [Fair Streaming Feature Selection](https://mlsanity.com/paper/2406.14401/) (2024)
- [A pipeline for enabling path-specific causal fairness in observational health data](https://mlsanity.com/paper/2601.09841/) (2026)
- [Fair Data Pre-Processing with Imperfect Attribute Space](https://mlsanity.com/paper/2603.26456/) (2026)

## Abstract (arXiv)

The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the primary steps to generate high quality training data, most of the fairness literature ignores this stage. In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach to identify a sub-collection of features that ensure the fairness of the dataset by performing conditional independence tests between different subsets of features. We use group testing to improve the complexity of the approach. We theoretically prove the correctness of the proposed algorithm to identify features that ensure interventional fairness and show that sub-linear conditional independence tests are sufficient to identify these variables. A detailed empirical evaluation is performed on real-world datasets to demonstrate the efficacy and efficiency of our technique.