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We propose a new nonparametric modeling framework for causal inference whenoutcomes depend on how agents are linked in a social or economic network. Suchnetwork interference describes a large literature on treatment spillovers,social interactions, social learning, information diffusion, disease andfinancial contagion, social capital formation, and more. Our approach works byfirst characterizing how an agent is linked in the network using theconfiguration of other agents and connections nearby as measured by pathdistance. The impact of a policy or treatment assignment is then learned bypooling outcome data across similarly configured agents. We demonstrate theapproach by proposing an asymptotically valid test for the hypothesis of policyirrelevance/no treatment effects and bounding the mean-squared error of ak-nearest-neighbor estimator for the average or distributional policyeffect/treatment response.

The Local Approach to Causal Inference under Network Interference