AI 中文总结
针对网络人群因果中介分析的无干扰假设缺陷，开发含处理与中介溢出效应的非参数框架，用图神经网络学习扰动函数，经模拟和中国农村农业保险实验验证了方法有效性。
AI 中文摘要
因果中介分析通常在无干扰假设下构建，该假设在网络人群中常被违反。我们针对单个大型观测网络开发了非参数框架，允许同时存在处理和中介溢出效应以及高维网络混杂。暴露和中介映射定义了因果估计量，不限制真实干扰机制，将自身效应与溢出效应分离，无需预先指定聚合模型。在强化的条件独立性条件下，我们识别出自身受控直接效应、自然直接效应和自然间接效应，并给出结构误差的原始充分条件。我们构建了增强逆概率加权估计量，其对受控效应具有双重稳健性，对自然效应具有多重稳健性，使用图神经网络从节点特征和邻接矩阵中学习高维扰动函数。在近似邻域干扰、弱网络依赖和合适的第一阶段速率下，我们建立了效应估计量的渐近正态性以及网络HAC方差估计量的一致性。模拟结果显示，图神经网络估计量优于基于手工构建邻域特征的机器学习方法；对中国农村农业保险实验的再分析发现，保险知识是重要中介渠道，而基于感知的中介变量则不是。
英文摘要
Causal mediation analysis is typically formulated under no interference, an assumption often violated in networked populations. We develop a nonparametric framework for a single large observed network that allows simultaneous treatment and mediator spillovers and high-dimensional network confounding. Exposure and mediator mappings define causal estimands without restricting the true interference mechanism, separating own from spillover effects without prespecified aggregation models. Under strengthened conditional independence conditions, we identify own controlled direct, natural direct, and natural indirect effects and give primitive sufficient conditions in terms of structural errors. We construct augmented inverse probability weighted estimators that are doubly robust for controlled effects and multiply robust for natural effects, using graph neural networks to learn high-dimensional nuisance functions from node features and the adjacency matrix. Under approximate neighborhood interference, weak network dependence, and suitable first-stage rates, we establish asymptotic normality of the effect estimators and consistency of a network HAC variance estimator. In simulations the graph neural network estimator outperforms machine learning methods built on hand-constructed neighborhood features, and a reanalysis of an agricultural insurance experiment in rural China finds insurance knowledge to be a substantive mediating channel while perception-based mediators are not.
Comments86 pages
