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

Towards Fair Graph Neural Networks via Graph Counterfactual