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.

通过识别具有因果效应的特征并区分因果关系和相关性，因果特征选择被认为是确保机器学习模型在高风险应用中担负伦理和社会责任的独特方法。

负责任机器学习的因果特征选择

Causal Feature Selection for Responsible Machine Learning

Causal feature selection has recently received increasing attention in
machine learning. Existing causal feature selection algorithms select unique
causal features of a class variable as the optimal feature subset. However, a
class variable usually has multiple states, and it is unfair to select the same
causal features for different states of a class variable. To address this
problem, we employ the class-specific mutual information to evaluate the causal
information carried by each state of the class attribute, and theoretically
analyze the unique relationship between each state and the causal features.
Based on this, a Fair Causal Feature Selection algorithm (FairCFS) is proposed
to fairly identifies the causal features for each state of the class variable.
Specifically, FairCFS uses the pairwise comparisons of class-specific mutual
information and the size of class-specific mutual information values from the
perspective of each state, and follows a divide-and-conquer framework to find
causal features. The correctness and application condition of FairCFS are
theoretically proved, and extensive experiments are conducted to demonstrate
the efficiency and superiority of FairCFS compared to the state-of-the-art
approaches.

提出了一种公平的因果特征选择算法，使用类特定的互信息评估每个类属性状态所包含的因果信息，以公正地确定每个类状态的因果特征。