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.

翻译：本文研究在网络溢出效应存在的情况下，为估计全局处理效应而设计的集群实验。我们提供了一个框架，用于选择能够最小化估计的全局效应在最坏情况下的均方误差的聚类方案。研究表明，最优聚类可转化为一个新颖的惩罚最小割优化问题，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在任意两种聚类设计（包括集群随机化与个体随机化之间的选择）间进行决策的简单条件。我们利用Facebook用户全域的独特网络数据以及一项现场实验的现有数据，展示了该方法的特性。