This article studies the benefits of using spatially randomized experimental designs which partition the experimental area into distinct, non-overlapping units with treatments assigned randomly. Such designs offer improved policy evaluation in online experiments by providing more precise policy value estimators and more effective A/B testing algorithms than traditional global designs, which apply the same treatment across all units simultaneously. We examine both parametric and nonparametric methods for estimating and inferring policy values based on this randomized approach. Our analysis includes evaluating the mean squared error of the treatment effect estimator and the statistical power of the associated tests. Additionally, we extend our findings to experiments with spatio-temporal dependencies, where treatments are allocated sequentially over time, and account for potential temporal carryover effects. Our theoretical insights are supported by comprehensive numerical experiments.

翻译：本文研究了采用空间随机化实验设计的优势。该设计将实验区域划分为互不重叠的独立单元，并对各单元随机分配处理。与传统全局设计（即对所有单元同时施以相同处理）相比，此类设计能在在线实验中提供更精确的政策价值估计量及更有效的A/B测试算法。我们分别探讨了基于该随机化方法的参数与非参数政策价值估计与推断方法。分析内容包括评估处理效应估计量的均方误差及相应检验的统计功效。此外，我们将研究结论拓展至具有时空依赖性的实验场景——即处理分配随时间序列展开，并纳入潜在的时间延续效应。上述理论发现得到了全面数值实验的验证。