  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.
