{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T07:14:33Z","timestamp":1787642073147,"version":"build-2736575974"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"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\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1559172"],"award-info":[{"award-number":["1559172"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1757140"],"award-info":[{"award-number":["1757140"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We provide adaptive inference methods, based on $\\ell _1$ regularization, for regular (semiparametric) and nonregular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of nonregular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak \u2018double sparsity robustness\u2019: either the approximation to the regression or the approximation to the representer can be \u2018completely dense\u2019 as long as the other is sufficiently \u2018sparse\u2019. Our main results are nonasymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.<\/jats:p>","DOI":"10.1093\/ectj\/utac002","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T11:16:20Z","timestamp":1650712580000},"page":"576-601","source":"Crossref","is-referenced-by-count":38,"title":["Debiased machine learning of global and local parameters using regularized Riesz representers"],"prefix":"10.1093","volume":"25","author":[{"given":"Victor","family":"Chernozhukov","sequence":"first","affiliation":[{"name":"Deparment of Economics, Massachusetts Institute of Technology , 50 Memorial Drive, Cambridge MA 02142, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Whitney K","family":"Newey","sequence":"additional","affiliation":[{"name":"Deparment of Economics, Massachusetts Institute of Technology , 50 Memorial Drive, Cambridge MA 02142, 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