{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:33:58Z","timestamp":1789752838166,"version":"4.0.1"},"reference-count":34,"publisher":"The Econometric Society","issue":"5","funder":[{"DOI":"10.13039\/100015607","name":"Stanford Institute for Economic Policy Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ECTA"],"abstract":"<jats:p>\n                    Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units. I approach the\n                    <jats:italic>spatial treatment<\/jats:italic>\n                    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\u2010based 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\u2010dimensional data in the theory, I extend a double machine learning result to the design\u2010based 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\u201019 shelter\u2010in\u2010place policies, finding a large positive effect at very short distances, with no effect at larger distances.\n                  <\/jats:p>","DOI":"10.3982\/ecta21571","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T11:53:17Z","timestamp":1789386797000},"page":"1529-1561","source":"Crossref","is-referenced-by-count":1,"title":["Causal Inference for Spatial Treatments"],"prefix":"10.3982","volume":"94","author":[{"given":"Michael","family":"Pollmann","sequence":"first","affiliation":[{"name":"Department of Economics, Duke 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