As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the strong ignorability assumption, which presumes the absence of hidden confounders-an assumption that is both difficult to validate and often unrealistic in practice. To address this issue, we propose CgNN, a novel approach that leverages network structure as instrumental variables (IVs), combined with graph neural networks (GNNs) and attention mechanisms, to mitigate hidden confounder bias and improve causal effect estimation. By utilizing network structure as IVs, we reduce confounder bias while preserving the correlation with treatment. Our integration of attention mechanisms enhances robustness and improves the identification of important nodes. Validated on two real-world datasets, our results demonstrate that CgNN effectively mitigates hidden confounder bias and offers a robust GNN-driven IV framework for causal inference in complex network data.

本研究针对网络中的因果推断问题，尤其是隐藏混杂因素对因果效应估计的影响，提出了CgNN方法。该方法通过将网络结构作为工具变量结合图神经网络和注意力机制，不仅减轻了混杂因素的偏倚，还增强了重要节点的识别能力。实验结果表明，CgNN在实际数据集上有效降低了隐藏混杂因素影响，对复杂网络数据的因果推断提供了坚实的框架。

因果GNN：一种基于GNN的工具变量方法用于网络中的因果推断