Causal Feature Selection for Responsible Machine Learning
It addresses the need for responsible ML in high-stakes applications by focusing on ethical and social values, but it is incremental as it surveys existing causal feature selection approaches.
This survey tackles the problem of ensuring machine learning models are responsible by addressing interpretability, fairness, adversarial robustness, and domain generalization, proposing causal feature selection as a method to identify features with causal impacts and avoid spurious correlations.
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.