We consider design-based causal inference in settings where randomized treatments have effects that bleed out into space in complex ways that overlap and in violation of the standard "no interference" assumption for many causal inference methods. We define a spatial "average marginalized effect," which characterizes how, in expectation, units of observation that are a specified distance from an intervention node are affected by treatment at that node, averaging over effects emanating from other intervention nodes. We establish conditions for non-parametric identification under unknown interference, asymptotic distributions of estimators, and recovery of structural effects. We propose methods for both sample-theoretic and permutation-based inference. We provide illustrations using randomized field experiments on forest conservation and health.

翻译：我们研究了在随机化处理效应以复杂方式在空间上扩散、重叠，且违反许多因果推断方法中标准的“无干扰”假设的情境下，基于设计的因果推断。我们定义了一种空间“平均边际效应”，该效应刻画了观测单位在期望意义上如何受到特定距离处的干预节点处理的影响，同时平均了来自其他干预节点的效应。我们建立了在未知干扰下非参数识别的条件、估计量的渐近分布以及结构效应的恢复方法。我们提出了基于样本理论和置换的推断方法，并通过森林保护与健康领域的随机现场实验进行了示例说明。