Abstract

Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its behavior. If the network grows according to either a latent community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. We show that, for common versions of those two network models, these estimates are so informative that controlling for estimated attributes allows for asymptotically unbiased and consistent estimation of social-influence effects in linear models. In particular, the bias shrinks at a rate that directly reflects how much information the network provides about the latent attributes. These are the first results on the consistent nonexperimental estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them.

摘要

社会影响不能从社交网络上的纯粹观察数据中识别出来，因为这种影响通常与潜在的同质性混淆，也就是说，节点的网络伙伴提供有关节点属性及其行为的信息。如果网络根据潜在社区（随机块）模型或连续潜在空间模型增长，则可以从社会关系的全局模式一致地估计潜在同质属性。我们表明，对于这两个网络模型的常见版本，这些估计提供的信息量如此之大，以至于控制估计的属性允许对线性模型中的社会影响效应进行渐近无偏和一致的估计。尤其，偏差缩小的速度直接反映了网络提供了多少关于潜在属性的信息。这些是在存在潜在同质性的情况下对社会影响效应进行一致的非实验估计的第一个结果，我们讨论了将它们推广的前景。