The Local Approach to Causal Inference under Network Interference

Eric Auerbach, Hongchang Guo and Max Tabord-Meehan

Papers from arXiv.org

Abstract: 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 deriving finite-sample bounds on the mean-squared error of a k-nearest-neighbor estimator for the average treatment response as well as proposing an asymptotically valid test for the hypothesis of policy irrelevance.

Date: 2021-05, Revised 2025-03
New Economics Papers: this item is included in nep-cdm, nep-ecm, nep-net and nep-ure
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Citations: View citations in EconPapers (3)

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