Causal Feature Selection for Responsible Machine Learning

TL;DR

This paper surveys causal feature selection methods to improve responsible machine learning by focusing on causality rather than correlation, aiming to enhance interpretability, fairness, robustness, and generalization.

Contribution

It provides a comprehensive overview of causal feature selection techniques and discusses their potential to address key issues in responsible ML.

Findings

Causal feature selection helps identify features with true causal impact.

It can reduce biases caused by spurious correlations.

Causal methods improve model robustness and fairness.

Abstract

Machine Learning (ML) has become an integral aspect of many real-world applications. As a result, the need for responsible machine learning has emerged, focusing on aligning ML models to ethical and social values, while enhancing their reliability and trustworthiness. Responsible ML involves many issues. This survey addresses four main issues: interpretability, fairness, adversarial robustness, and domain generalization. Feature selection plays a pivotal role in the responsible ML tasks. However, building upon statistical correlations between variables can lead to spurious patterns with biases and compromised performance. This survey focuses on the current study of causal feature selection: what it is and how it can reinforce the four aspects of responsible ML. By identifying features with causal impacts on outcomes and distinguishing causality from correlation, causal feature selection is posited as a unique approach to ensuring ML models to be ethically and socially responsible in high-stakes applications.