中文摘要
许多事件与政策（处理）发生在特定的空间位置，研究者对其对邻近单元的效应感兴趣。我从实验的视角探讨空间处理设定：我们会设计怎样的理想实验来估计空间处理的因果效应？这一视角促使我们将邻近已实现处理位置的单元与邻近反事实（未实现）候选位置的单元进行比较，这有别于当前的实证研究实践。我推导了基于设计的标准误，其易于计算。对于观测数据，当可观测特征（而非潜在结果）决定处理概率时，我提出了寻找反事实候选位置的机器学习方法。为了在理论中容纳针对高维数据的方法，我将双机器学习结果扩展至带有空间相关性的基于设计的框架。我将所提方法应用于研究COVID-19居家避疫政策期间杂货店对附近商家客流量的因果效应，发现在极短距离上存在大的正效应，而在较大距离上无效应。
Abstract
Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. 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 units near realized treatment locations and units near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design‐based standard errors that are straightforward to compute. For observational data, I propose machine learning methods to find counterfactual candidate locations when observable characteristics, rather than potential outcomes, determine treatment probabilities. To accommodate methods for high‐dimensional data in the theory, I extend a double machine learning result to the design‐based framework with spatial correlations. 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 large positive effect at very short distances, with no effect at larger distances.