Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influence of hidden confounders, which remain unobserved at the individual unit level. To address this limitation, researchers have utilized graph neural networks to aggregate neighbors' features to capture the hidden confounders and mitigate confounding bias by minimizing the discrepancy of confounder representations between the treated and control groups. Despite the success of these approaches, practical scenarios often treat all features as confounders and involve substantial differences in feature distributions between the treated and control groups. Confusing the adjustment and confounder and enforcing strict balance on the confounder representations could potentially undermine the effectiveness of outcome prediction. To mitigate this issue, we propose a novel framework called the \textit{Graph Disentangle Causal model} (GDC) to conduct ITE estimation in the network setting. GDC utilizes a causal disentangle module to separate unit features into adjustment and confounder representations. Then we design a graph aggregation module consisting of three distinct graph aggregators to obtain adjustment, confounder, and counterfactual confounder representations. Finally, a causal constraint module is employed to enforce the disentangled representations as true causal factors. The effectiveness of our proposed method is demonstrated by conducting comprehensive experiments on two networked datasets.

翻译：从观测数据中估计个体处理效应（ITE）是跨多个领域的关键任务。然而，现有许多关于ITE估计的研究忽略了隐藏混杂因子的影响，这些混杂因子在个体单元层面未被观测到。为应对这一局限，研究者已利用图神经网络聚合邻居特征以捕捉隐藏混杂因子，并通过最小化处理组与对照组间混杂因子表征的差异来减轻混杂偏倚。尽管这些方法取得了成功，但实际场景往往将所有特征视为混杂因子，且处理组与对照组间的特征分布存在显著差异。混淆调整因子与混杂因子并对混杂因子表征施加严格平衡，可能会削弱结果预测的有效性。为缓解此问题，我们提出一种名为\textit{图解耦因果模型}（GDC）的新框架，用于在网络设置下进行ITE估计。GDC利用因果解耦模块将单元特征分离为调整因子与混杂因子表征。随后，我们设计了一个由三个独立图聚合器组成的图聚合模块，以获取调整因子、混杂因子及反事实混杂因子表征。最后，采用因果约束模块确保解耦后的表征符合真实因果因子。通过在两个网络数据集上进行全面实验，验证了所提方法的有效性。