{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T02:27:40Z","timestamp":1787884060529,"version":"build-2784847793"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"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":[[2025,1,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Weighting methods are popular tools for estimating causal effects, and assessing their robustness under unobserved confounding is important in practice. Current approaches to sensitivity analyses rely on bounding a worst-case error from omitting a confounder. In this paper, we introduce a new sensitivity model called the variance-based sensitivity model, which instead bounds the distributional differences that arise in the weights from omitting a confounder. The variance-based sensitivity model can be parameterized by an R2 parameter that is both standardized and bounded. We demonstrate, both empirically and theoretically, that the variance-based sensitivity model provides improvements on the stability of the sensitivity analysis procedure over existing methods. We show that by moving away from worst-case bounds, we are able to obtain more interpretable and informative bounds. We illustrate our proposed approach on a study examining blood mercury levels using the National Health and Nutrition Examination Survey.<\/jats:p>","DOI":"10.1093\/biomet\/asae040","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T03:10:39Z","timestamp":1722568239000},"source":"Crossref","is-referenced-by-count":17,"title":["Variance-based sensitivity analysis for weighting estimators results in more informative bounds"],"prefix":"10.1093","volume":"112","author":[{"given":"Melody","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Political Science, Yale University , PO Box 208301 , New Haven, Connecticut 06520,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0409-6586","authenticated-orcid":false,"given":"Samuel D","family":"Pimentel","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California , 367 Evans Hall , Berkeley, California 94720,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"2025012017213606800_asae040-B1","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/biomet\/ass064","article-title":"Interval estimation of population means under unknown but bounded probabilities of sample selection","volume":"100","author":"Aronow","year":"2013","journal-title":"Biometrika"},{"key":"2025012017213606800_asae040-B2","first-page":"100","volume-title":"Proc. 15th Int. 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