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：本文研究了在存在网络溢出效应的情况下，为估计全局处理效应而设计的聚类实验。我们提出了一个框架，用于选择能最小化估计全局效应最坏情况均方误差的聚类方法。研究表明，最优聚类可归结为一个新颖的带惩罚项的图最小割优化问题，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两个聚类设计方案（包括聚类级随机化与个体级随机化的选择）之间进行取舍的简单条件。我们利用来自Facebook全用户网络的独特数据及一项现场实验的现有数据，展示了该方法的性质。