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Causal clustering: design of cluster experiments under network interference

Tuesday, April 2, 2024 - 09:30 to 10:30
Lihua Lei, Assistant Professor of Economics at the Graduate School of Business / Assistant Professor, by courtesy, of Statistics, Stanford University
Statistics Seminar
ESB 4192 / Zoom

To join this seminar virtually: Please request Zoom connection details from ea [at]

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.

Link to paper: