Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations
该研究提出在社交网络中，通过潜在社区或连续潜在空间模型估计同质性属性，从而在控制这些估计后实现因果同伴影响的无偏一致估计，解决了观测数据中影响与同质性混淆的问题。
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.