{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:36:56Z","timestamp":1790181416687,"version":"4.1.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric; hence, the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.<\/jats:p>","DOI":"10.1093\/biomet\/asae008","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T02:34:47Z","timestamp":1708396487000},"page":"537-550","source":"Crossref","is-referenced-by-count":8,"title":["Promises of parallel outcomes"],"prefix":"10.1093","volume":"111","author":[{"given":"Ying","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Connecticut , 215 Glenbrook Road , Storrs, Connecticut 06269, U.S.A. yzhou@uconn.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingke","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto , 700 University Avenue , Toronto, Ontario M5G 1X6, Canada , dingke.tang@mail.utoronto.ca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3480-4915","authenticated-orcid":false,"given":"Dehan","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto , 700 University Avenue , Toronto, Ontario M5G 1X6, Canada , dehan.kong@utoronto.ca"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9728-5249","authenticated-orcid":false,"given":"Linbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto , 700 University Avenue , Toronto, Ontario M5G 1X6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"2024051307573070400_asae008-B1","first-page":"111","volume-title":"Proc. 3rd Berkeley Symp. 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