Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion proposed by He and Song (2022, Preprint, arXiv:1812.04195v4 [stat.ME]), provides a method of decomposition analysis to measure the role of the covariates on the estimated diffusion, and develops an asymptotic inference procedure for the decomposition analysis in such a situation. This paper also presents results from a Monte Carlo study on the small sample performance of the inference procedure.

网络上的扩散是指一个横截面单元在一个时期内的状态变化导致下一时期网络中其邻居状态发生变化的现象。人们可以通过根据数据估计邻居之间随时间推移的横截面聚合相关性来估计或测试扩散。然而，如果扩散被遗漏的协变量混淆，估计的扩散可能会产生误导。本文着重于 He 和 Song (2022, Preprint, arXiv:1812.04195v4 [stat.ME]) 提出的扩散度量，提供了一种分解分析方法来衡量协变量对估计扩散的作用，并开发了一种在这种情况下进行分解分析的渐近推理程序。