Optimal Design of Experiments in the Presence of Interference
研究了当个体间存在干扰（即一个人的结果受组内其他人影响）时，如何最优设计随机饱和实验，并推导出检测平均处理效应的统计功效与识别新处理效应和溢出效应的能力之间的权衡关系。
Abstract We formalize the optimal design of experiments when there is interference between units, that is, an individual’s outcome depends on the outcomes of others in her group. We focus on randomized saturation designs, two-stage experiments that first randomize treatment saturation of a group, then individual treatment assignment. We map the potential outcomes framework with partial interference to a regression model with clustered errors, calculate standard errors of randomized saturation designs, and derive analytical insights about the optimal design. We show that the power to detect average treatment effects declines precisely with the ability to identify novel treatment and spillover effects.