{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T01:36:42Z","timestamp":1790991402360,"version":"4.1.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"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":[[2023,2,11]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>In many modern settings, such as an online marketplace, randomized experiments need to be executed over multiple time periods. In such temporal experiments, it has been observed that the effects of an intervention on an experimental unit may be large when the unit is first exposed to it, but then it attenuates after repeated exposures. This is typically due to units\u2019 habituation to the intervention, or some other form of learning, such as when users gradually start to ignore repeated mails sent by a promotional campaign. This paper proposes randomized designs for estimating causal effects in temporal experiments when habituation is present. We show that our designs are minimax optimal in a large class of practical designs. Our analysis is based on the randomization framework of causal inference, and imposes no parametric modelling assumptions on the outcomes.<\/jats:p>","DOI":"10.1093\/biomet\/asac024","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T15:11:45Z","timestamp":1651504305000},"page":"155-168","source":"Crossref","is-referenced-by-count":16,"title":["Minimax designs for causal effects in temporal experiments with treatment habituation"],"prefix":"10.1093","volume":"110","author":[{"given":"Guillaume W","family":"Basse","sequence":"first","affiliation":[{"name":"Stanford University Department of Statistics, , 390 Stanford Way, Stanford, California 94305-4020, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, , 32 Vassar Street, Cambridge, Massachusetts 02139, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panos","family":"Toulis","sequence":"additional","affiliation":[{"name":"University of Chicago Booth School of Business, , 5807 S. 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