  We describe semiparametric estimation and inference for causal effects using
observational data from a single social network. Our asymptotic results are the
first to allow for dependence of each observation on a growing number of other
units as sample size increases. In addition, while previous methods have
implicitly permitted only one of two possible sources of dependence among
social network observations, we allow for both dependence due to transmission
of information across network ties and for dependence due to latent
similarities among nodes sharing ties. We propose new causal effects that are
specifically of interest in social network settings, such as interventions on
network ties and network structure. We use our methods 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 we find no evidence for causal peer effects.
