  We study a continuous treatment effect model in the presence of treatment
spillovers through social networks. We assume that one's outcome is affected
not only by his/her own treatment but also by a (weighted) average of his/her
neighbors' treatments, both of which are treated as endogenous variables. Using
a control function approach with appropriate instrumental variables, we show
that the conditional mean potential outcome can be nonparametrically
identified. We also consider a more empirically tractable semiparametric model
and develop a three-step estimation procedure for this model. As an empirical
illustration, we investigate the causal effect of the regional unemployment
rate on the crime rate.
