Title: Design and analysis of experiments in networks
Abstract: Random assignment of individuals to treatments is often used to predict what will happen if the treatment is applied to everyone, but resulting estimates can suffer substantial bias in the presence of peer effects (i.e., interference, spillovers, social interactions). We describe experimental designs that reduce this bias by producing treatment assignments that are correlated in the network. For example, we can use graph partitioning methods to construct clusters of individuals who are then assigned to treatment or control together. This clustered assignment alone can substantially reduce bias, as can incorporating information about peers' treatment assignments or behaviors into the analysis. Simulation results show how this bias reduction varies with network structure and the size of direct and peer effects. We illustrate this method with real experiments, including a large experiment on Thanksgiving Day 2012.