提出正交学习框架，精准估计网络中个体与邻近节点的异质因果效应。
Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
- 分两阶段：先用图神经网络捕捉复杂依赖关系，再通过注意力模型估计因果效应。
- 可同时获得边级溢出效应、节点及总体水平的因果估计，且误差影响更小。
- 支持可解释分析，适合研究社交网络、政策扩散等场景中的影响传播。
在网络中估计因果效应面临挑战：处理措施不仅影响自身，还可能影响邻居，而网络同质性导致依赖和混杂。当因果效应在节点和边间存在异质性时，问题更为突出。本文提出一种两阶段正交学习框架，用于估计网络上的异质直接效应与溢出效应。第一阶段利用图神经网络估计包含协变量与网络结构复杂依赖的无用成分；第二阶段对这些无用成分进行残差化，并通过可解释的注意力干扰模型估计因果效应，实现边级溢出效应估计以及节点和总体水平的汇总结果。奈曼正交化与交叉拟合降低了对第一阶段估计误差的敏感性，使无用成分误差仅以高阶形式影响结果。进一步发展了基于自助法的不确定性量化方法，支持对估计的溢出矩阵进行点对点与整体推断。实验表明，该方法提升了异质效应估计精度，并支持如关键邻居检测与溢出符号恢复等可解释下游分析。
原文摘要 · Abstract (English)
Estimating causal effects on networks is challenging because treatments may affect both treated units and their neighbors, while network homophily induces dependence and confounding. These challenges are amplified when causal effects are heterogeneous across units and edges. We propose a two-stage orthogonal learning framework for estimating heterogeneous direct and spillover effects on networks. The first stage uses graph neural networks to estimate nuisance components that capture complex dependence on covariates and network structure. The second stage residualizes these nuisance components and estimates causal effects through an interpretable attention-based interference model, yielding edge-level spillover estimates as well as node- and population-level summaries. Neyman orthogonalization and cross-fitting reduce sensitivity to first-stage estimation error, so nuisance errors enter only at higher order. We further develop a bootstrap-based uncertainty quantification procedure for the estimated spillover matrix, enabling pointwise and simultaneous inference for heterogeneous edge- and node-level effects. Experiments show that our method improves heterogeneous effect estimation while supporting interpretable downstream analyses such as influential-neighbor detection and spillover-sign recovery.
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