Multiple randomization designs: estimation and inference with interference
针对市场环境中存在复杂溢出效应的随机实验，提出了一类新的多重随机化设计，并推导了有限样本下估计量的性质及中心极限定理。
Abstract Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this article, we derive the finite-sample properties of tractable estimators for ‘Simple Multiple Randomization Designs’, a new class of experimental designs which account for complex spillover effects in randomized experiments. Our derivations are obtained under a natural and general form of cross-unit interference, which we call ‘local interference’. We discuss the estimation of main effects, direct effects, and spillovers, and present associated central limit theorems.