{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:30:25Z","timestamp":1754159425633,"version":"3.41.2"},"reference-count":56,"publisher":"The Econometric Society","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["QE"],"abstract":"<jats:p>Many policies involve dynamics in their treatment assignments where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or quasi\u2010experimental study while satisfying exogenous constraints on policies. The paper proposes two estimation methods: one solves the treatment assignment problem sequentially through backward induction, and the other solves the entire problem simultaneously across all stages. We establish finite\u2010sample upper bounds on worst\u2010case average welfare regrets for these methods and show their optimal \n            <jats:italic>n<\/jats:italic>\n            <jats:sup>\u22121\/2<\/jats:sup> convergence rates. We also modify the simultaneous estimation method to accommodate intertemporal budget\/capacity constraints.\n          <\/jats:p>","DOI":"10.3982\/qe2288","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T08:54:29Z","timestamp":1753433669000},"page":"981-1022","source":"Crossref","is-referenced-by-count":1,"title":["Estimation of optimal dynamic treatment assignment rules under policy constraints"],"prefix":"10.3982","volume":"16","author":[{"given":"Shosei","family":"Sakaguchi","sequence":"first","affiliation":[{"name":"Faculty of Economics, The University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2359","published-online":{"date-parts":[[2025]]},"reference":[{"key":"quan200383-bib-0001","doi-asserted-by":"crossref","unstructured":"Aaberge, Rolf, Tarjei Havnes, and Magne Mogstad (2013), \u201cA theory for ranking distribution functions.\u201d Available at SSRN 2363225.","DOI":"10.2139\/ssrn.2363225"},{"key":"quan200383-bib-0002","doi-asserted-by":"publisher","DOI":"10.7910\/DVN\/SIWH9F"},{"key":"quan200383-bib-0003","unstructured":"Angrist, Josh (2009), \u201cMHE data archive.\u201d: URL: https:\/\/economics.mit.edu\/people\/faculty\/josh--angrist\/mhe--data--archiveURL, https:\/\/economics.mit.edu\/people\/faculty\/josh-angrist\/mhe-data-archive. 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