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.

翻译：本文研究在有单一网络溢出效应的情况下，设计集群实验以估计全局处理效应。我们提供了一个计量经济学框架，用于选择能够最小化估计全局处理效应的最坏情况均方误差的聚类方案。我们证明，最优聚类可以近似为一个新型惩罚最小割优化问题的解，该问题可通过现成的半定规划算法求解。我们的分析还刻画了易于检验的条件，用于在集群级随机化与个体级随机化之间做出选择。我们利用来自Facebook用户全体的独特网络数据以及现有田野实验的网络数据，展示了该方法的应用特性。