The Local Approach to Causal Inference under Network Interference

We propose a new unified framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. In the paper, we propose a new nonparametric modeling approach and consider two applications to causal inference. The first application is to testing policy irrelevance/no treatment effects. The second application is to estimating policy effects/treatment response. We conclude by evaluating the finite-sample properties of our estimation and inference procedures via simulation.