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The Local Approach to Causal Inference under Network Interference

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  • Eric Auerbach
  • Max Tabord-Meehan

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 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.

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  • Eric Auerbach & Max Tabord-Meehan, 2021. "The Local Approach to Causal Inference under Network Interference," Papers 2105.03810, arXiv.org, revised Jun 2023.
  • Handle: RePEc:arx:papers:2105.03810
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    References listed on IDEAS

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    Cited by:

    1. Alejandro Sanchez-Becerra, 2022. "The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment," Papers 2209.14391, arXiv.org.
    2. Michael P. Leung, 2024. "Causal Interpretation of Estimands Defined by Exposure Mappings," Papers 2403.08183, arXiv.org, revised Mar 2024.
    3. Anish Agarwal & Sarah H. Cen & Devavrat Shah & Christina Lee Yu, 2022. "Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference," Papers 2210.11355, arXiv.org, revised Oct 2023.

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