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Random Assignment with Nonrandom Peers

A Structural Approach to Counterfactual Treatment Assessment

Bibliographic Data

ID19314697
AuthorsAlan Griffith (0000-0001-5537-768X, University of Washington, corresponding author)
Year2024
Volume106
Issue3
Pages859-871
Publication date2024-05-14
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueThe Review of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0034-6535 • E-ISSN: 1530-9142
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/rest_a_01197
OpenAlexW4280587356
LanguageEN
Citations received3
References cited33

Efforts to leverage peer effects by changing assignment have often fallen short due to endogenous peer choice. To address this, I build a two-part model: agents form networks via continuous linking decisions; conditional on realized networks, outcomes are determined. I provide results on identification of both parts of the model. Using data from a randomized study in India, I estimate the model, assess its performance in out-of-sample prediction, and simulate outcomes under preferential assignment rules. This paper contributes new methodology for identifying effects of alternative assignments in the presence of network endogeneity, as well as identification of network formation models

Counterfactual thinking · Econometrics · Economics · Endogeneity · Identification (biology) · Instrumental variable · Leverage (statistics) · Machine learning · Peer effects · Random assignment · Randomized experiment · Sample (material) · Statistics · Advanced Causal Inference Techniques · Computer Science · Mathematics · Microfinance and Financial Inclusion · Psychology

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Unique citing works3
Citations per year0.6
Citation span2021 - 2026 (6)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 2

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