{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:56:21Z","timestamp":1793123781404,"version":"4.5.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"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":[[2021,5,15]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Na\u00efve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.<\/jats:p>","DOI":"10.1093\/biomet\/asaa071","type":"journal-article","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T19:13:19Z","timestamp":1598641999000},"page":"321-334","source":"Crossref","is-referenced-by-count":14,"title":["Inference for treatment effect parameters in potentially misspecified high-dimensional models"],"prefix":"10.1093","volume":"108","author":[{"given":"Oliver","family":"Dukes","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stijn","family":"Vansteelandt","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"2021051519490463900_B1","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1111\/rssb.12268","article-title":"Approximate residual balancing: Debiased inference of average treatment effects in high dimensions","volume":"B 80","author":"Athey,","year":"2018","journal-title":"J. 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