pathogens, behaviors, or ideas of scientific or societal interest. Transmission on the network can only occur through ties that connect exposed and unexposed individuals and given that the structure of the network constrains pairwise transmissions, the outcome of such a process must depend on the structure of the network. In general, the relationship between network struc- ture and contagion processes can be highly complex$^{1,2}$, and it is not known which network properties are likely confounding factors or useful for improving efficiency of estimation. This paper investigates the question of whether incorporating infor- mation about contact network structure, and summaries of contagion process outcomes at baseline, can be used to improve the accuracy of estimating exposure effects of outcomes that are the result of a contagion process operating on a network, in our case the spread of a novel microfinance program in a collection of villages in India. To address the issues of correlation of the outcome (uptake of microfinancing) within villages and the potential for confounding (including that related to the social network in these villages), we develop and apply methods that treat the data as resulting from a contagion process within villages. Confounding may arise at both the individual and cluster level and the methods we present below can accommodate either or both of these possibilities. Regardless of whether a study is observational or randomized, as long as individual out- comes are correlated only within discrete and independent clusters but not across them, estimates of the treatment or exposure effect that ignore correlation may still be unbiased. Consistent estimation of the variance must adjust for within-cluster out- come correlation$^{3,4,5}$. Of note, even in randomized studies, contact network information can improve statistical efficiency of estimation$^{6}$. To accommodate the microfinance outcomes that are correlated within clusters, we make use of generalized estimating equa- tions (GEEs) 7 that provide estimates of the average marginal treatment effect across all clusters in cluster randomized trials or in settings characterized by a strong correlation across groups of individuals$^{4}$. In particular, we consider the doubly-robust (DR) augmented GEE$^{8}$, which allows for use of baseline variables to adjust for confounding and improve efficiency. This estimator includes two user-specified models, an outcome model and an exposure model (i.e., propensity score or PS model). If either model is correctly specified, the estimate of the average exposure effect will be unbiased in expectation. Furthermore, the variance of this estimate decreases asymptotically if covariates are included in the outcome model that are related to the outcome even when they are not confounders. Other semiparametric, doubly-robust approaches with potential efficiency gains have been developed recently, such as targeted maximum likelihood estimation (TMLE) for clustered data$^{9}$. Although our investigation focuses on bias and efficiency using the doubly-robust augmented GEE, the issues we discuss are relevant for the TMLE approach as well. The paper is structured as follows. Section 2 provides some background on networks and presents the details of the estimation procedure, including how to incorporate contact network structure in the estimation process. Section 3 applies this approach to an analysis of the spread of a microfinance program within a collection of villages in India. Section 4 demonstrates the ability of our methods to adjust for confounding and to evaluate the potential gains in efficiency under different conditions in a simulation study that considers a range of settings some of which resemble that of the study in India. Section 5 provides our 2