Causal Inference for Spatial Treatments
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I propose a framework, estimators, and inference procedures for the analysis of causal effects in a setting with spatial treatments. Many events and policies (treatments), such as opening of businesses, building of hospitals, and sources of pollution, occur at specific spatial locations, with researchers interested in their effects on nearby individuals or businesses (outcome units). However, the existing treatment effects literature primarily considers treatments that could be assigned directly at the level of the outcome units, potentially with spillover effects. I approach the spatial treatment setting from a similar 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 unrealized candidate locations, which is distinct from current empirical practice. Furthermore, I show how to find such candidate locations and apply the proposed methods with observational data. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 lockdowns.
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