Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module.

翻译：图神经网络（GNNs）通过有效建模图结构数据中的复杂相互关系，展现了其重要性。为了提升GNNs的可信度和鲁棒性，增强它们捕捉因果关系的能力变得尤为关键。然而，尽管近期的一些进展确实从因果学习角度强化了GNNs，但针对GNNs因果建模能力的深入分析仍是一个未解决的问题。为了从因果学习视角全面分析各种GNN模型，我们构建了一个人工合成的数据集，其中数据与标签之间存在已知且可控的因果关系。通过理论基础进一步确保了生成数据的合理性。借鉴使用我们的数据集进行的分析得出的见解，我们引入了一个轻量级且高适应性的GNN模块，旨在跨多样化任务增强GNNs的因果学习能力。通过在合成数据集和其他真实世界数据集上进行的一系列实验，我们经验性地验证了所提模块的有效性。