Table 4: Change in percentage point RMSE reduction when changing simulation assumptions. Rows display a simulation assumption, and columns display an adjustment feature or strategy. Comparison to Microfinance Study Analysis As mentioned above, we set up the simulation study so that one of the settings closely matched the characteristics observed in the microfinance study. In that dataset, the average degree of participants is high ( k = 16 . 0 , averaged across all villages), and the degree distribution is not strongly right skewed; further details are provided in Section 5 of supplementary material. Mean degree assortativity is positive and moderately strong (0.346). Leaders were encouraged to inform all individuals they knew about the microfinance loan program, which would be expected to induce degree affectivity. Finally, individuals who both were leaders and participated in the microfinance program total 3.1% of the study population, suggesting a low exposure at baseline. Section 4 of the supplementary material (scenario a=(111110)) shows the simulation performance metrics for these observations. This simulation configuration exhibits good statistical coverage of interval estimates and a substantial improvement in the RMSE for each adjustment strategy, especially when using a stepwise selection of covariates in the outcome model. The outcome model in the microfinance data analysis included network and sociodemographic covariates for Exposure 1 and led to a 46% reduction in estimated standard error, which is fairly similar to the 53% reduction in the closest-matching simulation setting. In a spreading process, contagion outcomes depend on contact network structure and spreading process dynamics. To estimate the marginal effect of exposure on the contagion process, one strategy to adjust for confounding and reduce the variation of the exposure effect estimate is to make use of the doubly-robust augmented GEE methods described here. We investigated the extent to which bias and RMSE reduction in exposure effect estimates depend on network and contagion properties. Adjusting for contact network features and baseline contagion reduced bias and yielded a considerable reduction in RMSE across a range 5 Discussion 15 X (9) All Step (Intercept) 27 36 36 High vs. Low Degree -1 -5 -5 Powerlaw vs. Poisson -19 -10 -10 Assortative vs. Disassortative 4 3 3 Communities vs. No Communities 2 -0 -0 Degree vs. Unit Infectivity -4 -18 -18 High vs. Low Baseline 29 34 34