This paper presents methods for analyzing spatial experiments when complex spillovers, displacement effects, and other types of "interference" are present. We present a robust, design-based approach to analyzing effects in such settings. The design-based approach derives inferential properties for causal effect estimators from known features of the experimental design, in a manner analogous to inference in sample surveys. The methods presented here target a quantity of interest called the "average marginalized response," which is equal to the average effect of activating a treatment at an intervention node that is a given distance away, averaging ambient effects emanating from other intervention nodes. We provide a step-by-step tutorial based on the SpatialEffect package for R. We apply the methods to a randomized experiment on payments for community forest conservation in Uganda, showing how our methods reveal possibly substantial spatial spillovers that more conventional analyses cannot detect.

翻译：本文提出了在存在复杂溢出效应、位移效应及其他类型“干扰”时分析空间实验的方法。我们提出了一种基于设计的稳健方法来分析此类情景中的效应。该基于设计的方法从实验设计的已知特征中推导因果效应估计量的推断性质，其原理类似于样本调查中的推断。本文的方法针对一个称为“平均边际响应”的感兴趣量，该量等于在给定距离的干预节点上激活处理所产生的平均效应，同时平均来自其他干预节点的环境效应。我们提供了基于R语言SpatialEffect包的逐步教程。将该方法应用于乌干达社区森林保护支付的随机实验，展示了该方法如何揭示传统分析无法检测的、可能显著的空间溢出效应。