  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.
