{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T12:50:27Z","timestamp":1785761427232,"version":"3.56.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,4]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by Rosenbaum (1993) estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum\u2019s estimator is robust against outliers with a breakdown point that uniformly dominates that of any weighted quantile estimator. When pretreatment covariates are available, a regression-adjusted version of Rosenbaum\u2019s estimator uses an agnostic linear regression on the covariates and bases inference on the ranks of residuals. Under mild integrability conditions, we show that this estimator is at most 13.6% less efficient, in the worst case, than the commonly used mean-based regression adjustment method proposed by Lin (2013), and often outperforms it when the residuals have heavy tails. Moreover, under suitable assumptions, Rosenbaum\u2019s regression-adjusted estimator is at least as efficient as the unadjusted one. Finally, we initiate the study of Rosenbaum\u2019s estimator when the constant treatment effect assumption may be violated. To analyse the regression-adjusted estimator, we develop local asymptotics of rank statistics under the design-based framework, which may be of independent interest.<\/jats:p>","DOI":"10.1093\/biomet\/asag028","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:43:47Z","timestamp":1775821427000},"source":"Crossref","is-referenced-by-count":0,"title":["Robustness and efficiency of Rosenbaum\u2019s rank-based estimator in randomized trials: a design-based perspective"],"prefix":"10.1093","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-1815","authenticated-orcid":false,"given":"Aditya","family":"Ghosh","sequence":"first","affiliation":[{"name":"Department of Statistics, Stanford University , 390 Jane Stanford Way , Stanford, California 94305,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabarun","family":"Deb","sequence":"additional","affiliation":[{"name":"Chicago Booth School of Business , 5807 S Woodlawn Avenue , Chicago, Illinois 60637, . nabarun.deb@chicagobooth.edu","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9336-5062","authenticated-orcid":false,"given":"Bikram","family":"Karmakar","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Wisconsin-Madison , 1205 University Avenue , Madison, Wisconsin 53706, . bkarmakar@wisc.edu","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bodhisattva","family":"Sen","sequence":"additional","affiliation":[{"name":"Department of Statistics, Columbia University , 1255 Amsterdam Avenue , New York, New York 10027, . b.sen@columbia.edu","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"2026080308113096000_asag028-B1","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1093\/jrsssb\/qkad072","article-title":"Semi-parametric estimation of treatment effects in randomised experiments","volume":"85","author":"Athey","year":"2023","journal-title":"J. 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