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In an observational study, obtaining unbiased estimates of an exposure effect requires adjusting for all potential confounders. 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 Karnataka, 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.
We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individua
Large scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the qu
We develop a theoretical framework for the study of epidemic-like social contagion in large scale social systems. We consider the most general setting in which different communication platforms or categories form multiplex networks. Specifically, we
No unmeasured confounding is often assumed in estimating treatment effects in observational data when using approaches such as propensity scores and inverse probability weighting. However, in many such studies due to the limitation of the databases,
The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time con