Causal Inference for Spatial Treatments

TL;DR

This paper develops a causal inference framework for spatial treatments, proposing new experimental designs, standard errors, and machine learning methods to estimate effects from observational data, demonstrated through grocery store impact analysis.

Contribution

It introduces a novel experimental perspective for spatial treatments, deriving design-based standard errors and extending double machine learning to spatially correlated data.

Findings

Grocery stores have a large positive effect on foot traffic at very short distances.

No significant effect of grocery stores on foot traffic at larger distances.

Proposed methods effectively estimate causal effects in spatial treatment settings.

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.