论文标题
通过主成分网络回归估算网络介导的因果效应
Estimating network-mediated causal effects via principal components network regression
论文作者
论文摘要
我们开发了一种将社交网络因果关系分解为由网络介导的间接效应的方法，而与社交网络无关的直接效应。为了处理网络结构的复杂性，我们假设潜在的社会群体充当因果媒介。我们开发主要组件网络回归模型，以区分社会效应与非社会效应。拟合回归模型与主成分分析一样简单，然后进行普通的最小二乘估计。我们证明了该过程中回归系数的渐近理论，并表明它是广泛适用的，可以在回归误差和网络边缘上进行各种分布。我们仔细地表征了使用回归模型进行因果推断所需的反事实假设，并表明当前的因果网络回归方法可能导致过度控制偏差。该方法非常笼统，因此它适用于社交网络以外的许多类型的结构化数据，例如文本，研究数据，心理计量学，图像和OMICS。
We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network, and a direct effect independent of the social network. To handle the complexity of network structures, we assume that latent social groups act as causal mediators. We develop principal components network regression models to differentiate the social effect from the non-social effect. Fitting the regression models is as simple as principal components analysis followed by ordinary least squares estimation. We prove asymptotic theory for regression coefficients from this procedure and show that it is widely applicable, allowing for a variety of distributions on the regression errors and network edges. We carefully characterize the counterfactual assumptions necessary to use the regression models for causal inference, and show that current approaches to causal network regression may result in over-control bias. The method is very general, so that it is applicable to many types of structured data beyond social networks, such as text, areal data, psychometrics, images and omics.
