Causal clustering: design of cluster experiments under network interference

Author:

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

Keyword:

Economics, Econometrics, Econometrics (econ.EM), Statistics Theory (math.ST), Methodology (stat.ME)

journal:

date:

2023-10-22 16:00:00

Abstract

This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose 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 network data from a field experiment.

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