  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.
