A0884
Title: Causal inference for social network data
Authors: Elizabeth Ogburn - Johns Hopkins University (United States) [presenting]
Abstract: Semiparametric estimation and inference are described for causal effects using observational data from a single social network. Our asymptotics allows for the dependence of each observation on a growing number of other units as sample size increases. Both dependencies are allowed due to the transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. New causal effects are proposed that are specifically of interest in social network settings, such as interventions on network ties and network structure. Our methods are used to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure, no evidence is found for causal peer effects. Supplementary materials for this article are available online.
