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Activity Number: 95 - Network Data Analysis
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #322371
Title: "The Local Approach to Causal Inference Under Network Interference"
Author(s): Eric Auerbach* and Max Tabord-Meehan
Companies: Northwestern University and University of Chicago
Keywords: Networks; Causal Inference; Spillovers; Nonparametric ; Interference; SUTVA

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.

Authors who are presenting talks have a * after their name.

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