{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:18:05Z","timestamp":1792793885086,"version":"4.5.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2018YFC2000302"],"award-info":[{"award-number":["2018YFC2000302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12171476"],"award-info":[{"award-number":["12171476"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guo Qiang Institute of Tsinghua University","id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071242"],"award-info":[{"award-number":["12071242"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive randomization, is routinely used in the design stage to balance treatment allocations with respect to a few variables that are most relevant to the outcomes. Then, regression is performed in the analysis stage to adjust the remaining imbalances to yield more efficient treatment effect estimators. Building upon and unifying recent results obtained for ordinary-least-squares adjusted estimators under covariate-adaptive randomization, this paper presents a general theory of regression adjustment that allows for model mis-specification and the presence of a large number of baseline covariates. We exemplify the theory on two lasso-adjusted treatment effect estimators, both of which are optimal in their respective classes. In addition, nonparametric consistent variance estimators are proposed to facilitate valid inferences, which work irrespective of the specific randomization methods used. The robustness and improved efficiency of the proposed estimators are demonstrated through numerical studies.<\/jats:p>","DOI":"10.1093\/biomet\/asac036","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T09:36:39Z","timestamp":1655458599000},"page":"431-447","source":"Crossref","is-referenced-by-count":24,"title":["Lasso-adjusted treatment effect estimation under covariate-adaptive randomization"],"prefix":"10.1093","volume":"110","author":[{"given":"Hanzhong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Tsinghua University Center for Statistical Science, , Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Tu","sequence":"additional","affiliation":[{"name":"Renmin University of China Institute of Statistics and Big Data, , Beijing 100872, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Ma","sequence":"additional","affiliation":[{"name":"Renmin University of China Institute of Statistics and Big Data, , Beijing 100872, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"2023051508564060300_B1","doi-asserted-by":"crossref","first-page":"7383","DOI":"10.1073\/pnas.1510506113","article-title":"Lasso adjustments of treatment effect estimates in randomized experiments","volume":"113","author":"Bloniarz,","year":"2016","journal-title":"Proc. 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