{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:10:46Z","timestamp":1778307046028,"version":"3.51.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,9]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:p>This paper extends difference-in-differences (DiD) to settings with continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of treatment intensity is identified under a conditional parallel trends assumption. Estimating the ATT in this framework requires first estimating infinite-dimensional nuisance parameters, particularly the conditional density of the continuous treatment, which can introduce substantial bias. To address this challenge, we propose estimators for the causal parameters under the double\/debiased machine learning framework and establish their asymptotic normality. Additionally, we provide consistent variance estimators and construct uniform confidence bands based on a multiplier bootstrap procedure. To demonstrate the effectiveness of our approach, we revisit a previous study on the 1983 Medicare Prospective Payment System reform, reframing it as a DiD with continuous treatment and non-parametrically estimating its effects.<\/jats:p>","DOI":"10.1093\/ectj\/utaf024","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T12:02:59Z","timestamp":1759406579000},"page":"256-276","source":"Crossref","is-referenced-by-count":0,"title":["Continuous difference-in-differences with double\/debiased machine learning"],"prefix":"10.1093","volume":"29","author":[{"given":"Lucas Zheng","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California Los Angeles Department of Economics, , 315 Portola Plaza, Los Angeles, CA 90095 ,","place":["USA"]},{"name":"Bates White Economic Consulting , 2001\u00a0K Street NW North Building, Suite 500, Washington, DC 20006 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