of simulation settings; such adjustment also reduced RMSE in the analysis of the spread of microfinance. In our models, covariates derived from network features differed in their usefulness in reducing exposure estimate variance. They also differed in how feasible their estimation is in practice. Obtaining the degree of an individual may be quite feasible using a simple survey, yet the gains we observed from using this covariate were modest. We found that the number of neighbors affected at baseline yielded the largest reduction in variance and may be feasible to estimate in some practical settings. This work invites several extensions. Information may be missing or misreported for individual outcomes, contact network data, or both, which may lead to bias or increased variation in estimating the exposure effect. We also did not estimate the effect of unmeasured confounding–an important consideration in observational studies. Although we carried out extensive simulations, they are neither intended to be, nor can they be, comprehensive. Simulation of other settings would be useful to help guide research studies in which network features may confound results of observational studies or may help improve efficiency of estimation in both randomized and observational studies. Degree assortative rewiring. This is performed by randomly selecting two edges within a block pair and rewiring them, as described in Algorithm 2. A diagram of this process is shown in Figure 3. To decrease assortativity, the inequality in Step 3 must be reversed. 6 Appendix 16