Causal clustering: design of cluster experiments under network interference

Davide Viviano, Lihua Lei, Guido Imbens, Brian Karrer, Okke Schrijvers and Liang Shi

Papers from arXiv.org

Abstract: This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.

Date: 2023-10, Revised 2025-01
New Economics Papers: this item is included in nep-des, nep-ecm, nep-exp and nep-net
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2310.14983

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