{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:06:22Z","timestamp":1790237182999,"version":"4.1.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"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":[[2020,4,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non-parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non-parametric model, and we propose multiply robust and locally efficient estimators when non-parametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post-licensure surveillance of vaccine safety among children.<\/jats:p>","DOI":"10.1111\/rssb.12361","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T02:51:22Z","timestamp":1579747882000},"page":"521-540","source":"Crossref","is-referenced-by-count":81,"title":["Multiply Robust Causal Inference with Double-Negative Control Adjustment for Categorical Unmeasured Confounding"],"prefix":"10.1093","volume":"82","author":[{"given":"Xu","family":"Shi","sequence":"first","affiliation":[{"name":"University of Michigan , Ann Arbor , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Miao","sequence":"additional","affiliation":[{"name":"Peking University , Beijing , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer C.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Washington Health Research Institute , Seattle , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric J.","family":"Tchetgen Tchetgen","sequence":"additional","affiliation":[{"name":"University of Pennsylvania , Philadelphia , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"2023022408363235800_","volume-title":"Efficient policy learning","author":"Athey","year":"2017"},{"key":"2023022408363235800_","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","article-title":"Doubly robust estimation in missing data and causal inference models","volume":"61","author":"Bang","year":"2005","journal-title":"Biometrics"},{"key":"2023022408363235800_","volume-title":"Efficient and Adaptive Inference in Semiparametric Models","author":"Bickel","year":"1993"},{"key":"2023022408363235800_","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1214\/10-AOS809","article-title":"Bootstrap consistency for general semiparametric M-estimation","volume":"38","author":"Cheng","year":"2010","journal-title":"Ann. 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