Community informed experimental design

Network information has become a common feature of many modern experiments. From vaccine efficacy studies to marketing for product adoption, stakeholders aim to estimate global treatment effects — what happens if everyone in a network is treated versus if no one is treated. Because individual outcomes are potentially influenced by the treatments or behaviors of others in the network, experimental designs must condition on the underlying network. Social networks frequently exhibit homophilous community structure, meaning that individuals within observed or latent communities are more similar to each. This observation motivates the development of community aware experimental design. This design recognizes that information between individuals likely flows along within community edges rather than across community edges. We demonstrate that this design reduces the bias of a simple difference in means estimator, even when the community structure of the graph needs to be estimated. Further, we show that as the community detection problem gets more difficult or if the community structure does not affect the causal question, the proposed design maintains its performance.