Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF. Our code is available at https://github.com/TimeLovercc/CAF-GNN.

翻译：图神经网络在图上表示学习中展现出强大能力，从而促进了各种任务的发展。尽管图神经网络在建模图上表现优异，但近期研究表明，它们倾向于继承并放大训练数据中的偏差，这引发了在高风险场景中采用图神经网络的担忧。因此，人们为构建公平感知的图神经网络付出了诸多努力。然而，现有的大多数公平图神经网络采用统计公平性概念来学习公平的节点表示，当出现统计异常时，这些方法可能无法有效缓解偏差。受因果理论启发，已有一些尝试利用图反事实公平性来消除不公平的根源。然而，这些方法存在通过扰动或生成得到的反事实样本不真实的问题。在本文中，我们采用因果视角审视公平图学习问题。在因果分析的指导下，我们提出一个名为CAF的新颖框架，该框架能从训练数据中选择反事实样本以避免不真实的反事实，并利用所选的反事实样本学习面向节点分类任务的公平节点表示。在合成数据集和真实数据集上的大量实验表明，CAF方法具有有效性。我们的代码可在https://github.com/TimeLovercc/CAF-GNN获取。