Spatially Randomized Designs Can Enhance Policy Evaluation

Abstract

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.