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Causal Spillover Effects Using Instrumental Variables

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  • Gonzalo Vazquez-Bare

Abstract

I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of peers' types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes.

Suggested Citation

  • Gonzalo Vazquez-Bare, 2020. "Causal Spillover Effects Using Instrumental Variables," Papers 2003.06023, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2003.06023
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    References listed on IDEAS

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    Cited by:

    1. Tadao Hoshino & Takahide Yanagi, 2021. "Causal Inference with Noncompliance and Unknown Interference," Papers 2108.07455, arXiv.org, revised Oct 2023.
    2. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.

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