Causal Feature Selection for Algorithmic Fairness

TL;DR

This paper introduces a causal feature selection method that enhances fairness in machine learning datasets by identifying unbiased features through conditional independence tests, without requiring a prior causal model.

Contribution

It proposes a novel approach to ensure dataset fairness during data integration by using causal interventional fairness principles and group testing, without needing a pre-existing causal model.

Findings

The method guarantees the identification of fair features theoretically.

Sub-linear conditional independence tests suffice for feature selection.

Empirical results show improved fairness and efficiency on real datasets.

Abstract

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.