论文标题
空间治疗的因果推断
Causal Inference for Spatial Treatments
论文作者
论文摘要
许多事件和政策（治疗）发生在特定的空间位置，研究人员对他们对附近感兴趣单位的影响感兴趣。我从实验的角度处理空间处理设置：我们将设计什么理想的实验来估计空间治疗的因果影响？这种观点促使人们在实现治疗位置附近的个人与反事实（未实现的）候选人位置的个人之间进行了比较，这与当前的经验实践不同。我得出了基于设计的标准误差，这些误差可直接计算，无论结果中的空间相关性如何。此外，我提出了机器学习方法，以在不满意的治疗分配给位置的情况下使用观察数据来查找反事实候选位置。我应用了提出的方法来研究杂货店在Covid-19的现场政策期间杂货店对附近企业流量的因果影响，在很短的距离处发现了实质性的积极效果，在较大距离的情况下没有效果。
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.
