  We propose a new nonparametric modeling 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, disease and
financial contagion, 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. We demonstrate the
approach by proposing an asymptotically valid test for the hypothesis of policy
irrelevance/no treatment effects and bounding the mean-squared error of a
k-nearest-neighbor estimator for the average or distributional policy
effect/treatment response.
