Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units of interest. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design-based standard errors that are straightforward to compute irrespective of spatial correlations in outcomes. Furthermore, I propose machine learning methods to find counterfactual candidate locations using observational data under unconfounded assignment of the treatment to locations. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies, finding a substantial positive effect at a very short distance, with no effect at larger distances.

翻译：许多事件和政策(处理)发生在特定空间地点,研究人员对这些活动和政策(处理)对附近感兴趣单位的影响感兴趣。我从实验的角度看待空间处理环境:我们设计什么理想的实验来估计空间处理的因果关系?这个角度促使对接近实际治疗地点的个人和接近反事实(未实现)候选地点的个人进行比较,这与目前的实践做法不同。我从设计上得出标准错误,不论结果的空间相关性如何,都直截了当地计算。此外,我提议采用机器学习方法,利用无根据地将观察数据作为治疗地点的分类,寻找反事实的候选地点。我采用拟议方法,在COVID-19住所内政策期间研究杂货店向附近企业的徒步运输造成的因果关系,在很短的距离内找到实质性的积极效果,在更远的距离内找不到效果。