arXiv 31 Oct 2020 · Econometrics · 6 citations (OpenAlex)
arXiv:2011.00373 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wang, Ye, Samii, Cyrus, Chang, Haoge, Aronow, P. M (2025) Design-based inference for spatial experiments under unknown interference | 1.000 | 11 | 3 | 100% |
| 2 | Imbens, Guido W., Rubin, Donald B (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction | 1.000 | 5 | 3 | 100% |
| 3 | Conley, Timothy G (1999) GMM estimation with cross sectional dependence | 0.874 | 14 | 2 | 100% |
| 4 | Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-… (2014) Generative Adversarial Nets | 0.737 | 3 | 2 | 100% |
| 5 | Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E (2012) ImageNet Classification with Deep Convolutional Neural Networks | 0.737 | 3 | 2 | 100% |
| 6 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 7 | Duflo, Esther (2001) Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment | 0.644 | 2 | 2 | 100% |
| 8 | Goodfellow, Ian (2016) NIPS 2016 tutorial: Generative adversarial networks | 0.644 | 2 | 2 | 100% |
| 9 | Linden, Leigh, Rockoff, Jonah E (2008) Estimates of the impact of crime risk on property values from Megan's laws | 0.644 | 2 | 2 | 100% |
| 10 | Lotter, William, Kreiman, Gabriel, Cox, David (2016) Unsupervised learning of visual structure using predictive generative networks | 0.644 | 2 | 2 | 100% |
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