{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T21:14:02Z","timestamp":1788988442109,"version":"build-2803163510"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"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\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. Our method consists of three steps. We first use a class of penalized $M$-estimators for the propensity score and outcome models. We then calibrate the initial estimate of the propensity score by balancing a carefully selected subset of covariates that are predictive of the outcome. Finally, the estimated propensity score is used to construct the inverse probability weighting estimator. We prove that the proposed estimator, which we call the high-dimensional covariate balancing propensity score, has the sample boundedness property, is root-$n$ consistent, asymptotically normal, and semiparametrically efficient when the propensity score model is correctly specified and the outcome model is linear in covariates. More importantly, we show that our estimator remains root-$n$ consistent and asymptotically normal so long as either the propensity score model or the outcome model is correctly specified. We provide valid confidence intervals in both cases and further extend these results to the case where the outcome model is a generalized linear model. In simulation studies, we find that the proposed methodology often estimates the average treatment effect more accurately than existing methods. We also present an empirical application, in which we estimate the average causal effect of college attendance on adulthood political participation. An open-source software package is available for implementing the proposed methodology.<\/jats:p>","DOI":"10.1093\/biomet\/asaa020","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T07:22:48Z","timestamp":1583479368000},"page":"533-554","source":"Crossref","is-referenced-by-count":55,"title":["Robust estimation of causal effects via a high-dimensional covariate balancing propensity score"],"prefix":"10.1093","volume":"107","author":[{"given":"Yang","family":"Ning","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Cornell University, 1188 Comstock Hall, 129 Garden Ave., Ithaca, New York 14853, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Sida","sequence":"additional","affiliation":[{"name":"Microsoft Research, 14865 NE 36th St., Redmond, Washington 98052, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kosuke","family":"Imai","sequence":"additional","affiliation":[{"name":"Department of Statistics, Harvard University, One Oxford Street, Cambridge, Massachusetts 02138, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"2020081714270133600_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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