论文标题
在网络混淆下的因果推断的图形神经网络
Graph Neural Networks for Causal Inference Under Network Confounding
论文作者
论文摘要
本文研究了来自单个大网络的观察数据的因果推断。我们考虑了一种非参数模型，该模型对潜在结果的干扰并选择治疗。这两个阶段都可能是同时方程模型的结果，这允许内源性同伴效应。这导致高维网络混淆，在所有单元的网络和协变量构成选择偏差的来源的情况下。相反，现有文献假定可以通过这些对象的已知低维函数来总结混杂的混淆。我们建议使用图形神经网络（GNN）来调整网络混淆。当干扰因网络距离衰减时，我们认为该模型具有低维结构，使估计可行并证明使用浅GNN架构的使用是合理的。
This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in potential outcomes and selection into treatment. Both stages may be the outcomes of simultaneous equation models, which allow for endogenous peer effects. This results in high-dimensional network confounding where the network and covariates of all units constitute sources of selection bias. In contrast, the existing literature assumes that confounding can be summarized by a known, low-dimensional function of these objects. We propose to use graph neural networks (GNNs) to adjust for network confounding. When interference decays with network distance, we argue that the model has low-dimensional structure that makes estimation feasible and justifies the use of shallow GNN architectures.
