Leveraging Contact Network Information in Clustered Observational Studies of Contagion Processes Patrick Staples ∗ $^{1}$, M´ elanie Prague † $^{1}$, Victor De Gruttola ‡ $^{1}$, and Jukka-Pekka Onnela § 1 $^{1}$Department of Biostatistics, Harvard University, Boston, MA 02115, USA In an observational study, obtaining unbiased estimates of an exposure effect requires adjusting for all potential con- founders. When this condition is met, leveraging additional covariates related to the outcome may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect exposed and unexposed individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as both confounders and efficiency covariates in exposure effect estimation. Using doubly-robust augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to estimate the effects of two distinct exposures, the proportion of leaders in a village and the proportion of households participating in a self-help program, for the spread of a microfinance program in a collection of villages in Kar- nataka, India. We compare these results to simulated observational trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. Contact networks capture the structure of possible pairwise transmissions (represented by network edges or ties) in a popu- lation of actors (represented by network nodes) for various types of contagion processes, which may describe the spread of 1 Introduction Abstract $^{∗}$email: patrickstaples@fas.harvard.edu $^{†}$email: mprague@hsph.harvard.edu $^{‡}$email: degrut@hsph.harvard.edu $^{§}$email: onnela@hsph.harvard.edu 1 arXiv:1610.00039v2 [stat.AP] 28 Aug 2018