Primary Submission Category: Causal Inference in Networks
Causal clustering: design of cluster experiments under network interference
Authors: Lihua Lei, Davide Viviano, Guido Imbens, Brian Karrer, Okke Schrijvers, Liang Shi,
Presenting Author: Lihua Lei*
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 statistical decision-theoretic 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.