The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variations of sensitive attributes, which is a crucial concept in addressing societal biases. However, existing counterfactual fairness approaches usually overlook intrinsic information about sensitive features, limiting their ability to achieve fairness while simultaneously maintaining performance. To tackle this challenge, we introduce EXOgenous Causal reasoning (EXOC), a novel causal reasoning framework motivated by exogenous variables. It leverages auxiliary variables to uncover intrinsic properties that give rise to sensitive attributes. Our framework explicitly defines an auxiliary node and a control node that contribute to counterfactual fairness and control the information flow within the model. Our evaluation, conducted on synthetic and real-world datasets, validates EXOC's superiority, showing that it outperforms state-of-the-art approaches in achieving counterfactual fairness.

翻译：机器学习模型在平衡公平性与预测准确性方面面临的挑战，尤其是在考虑种族、性别或年龄等敏感属性时，近年来引发了大量研究。反事实公平性确保预测在敏感属性的反事实变化中保持一致，这是解决社会偏见的关键概念。然而，现有的反事实公平方法通常忽略敏感特征的内在信息，限制了其在保持性能的同时实现公平性的能力。为应对这一挑战，我们提出了EXOgenous Causal reasoning（EXOC），一种受外生变量启发的新型因果推理框架。该框架利用辅助变量揭示产生敏感属性的内在特性。我们明确定义了辅助节点与控制节点，二者共同促进反事实公平性并控制模型内部的信息流。在合成数据集和真实数据集上的评估验证了EXOC的优越性，表明其在实现反事实公平性方面超越了现有最先进方法。