用图结构当工具变量，提升网络数据因果推断准确性
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
- 用图神经网络和注意力机制，将网络结构作工具变量
- 在真实数据上显著降低隐藏混杂偏误
- 适合做网络因果分析的研究者使用
随着网络数据应用不断扩展，网络内的因果推断受到越来越多关注。然而，隐藏混杂因素使得因果效应估计变得复杂。现有方法多依赖强可忽略性假设，即假设不存在隐藏混杂因素——这一假设既难以验证，也常不切实际。为此，我们提出CgNN，一种利用网络结构作为工具变量（IVs），结合图神经网络（GNNs）与注意力机制的新方法，以缓解隐藏混杂偏误并提升因果效应估计精度。通过将网络结构作为工具变量，我们降低了混杂偏误，同时保持其与处理变量的相关性。注意力机制的引入增强了模型鲁棒性，并提升了关键节点的识别能力。在两个真实世界数据集上的验证表明，CgNN能有效缓解隐藏混杂偏误，为复杂网络数据中的因果推断提供了一个稳健的GNN驱动工具变量框架。
原文摘要 · Abstract (English)
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.
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