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Causal Inference for Spatial Treatments

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  • Michael Pollmann
Abstract
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.

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  • Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2011.00373
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