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However, for nonrandomized exposures, the influence function based variance estimator frequently used with doubly robust estimators of the average causal effect is only consistent when both working models (ie, outcome and exposure models) are correctly specified. Here, the empirical sandwich variance estimator and the nonparametric bootstrap are demonstrated to be doubly robust variance estimators. That is, they are expected to provide valid estimates of the variance leading to nominal confidence interval coverage when only 1 working model is correctly specified. Simulation studies illustrate the properties of the influence function based, empirical sandwich, and nonparametric bootstrap variance estimators in the setting where parametric working models are assumed. Estimators are applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP) study to estimate the effect of maternal anemia on birth weight among women with HIV.<\/jats:p>","DOI":"10.1093\/biomtc\/ujaf054","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T09:04:45Z","timestamp":1746435885000},"source":"Crossref","is-referenced-by-count":7,"title":["Double robust variance estimation with parametric working models"],"prefix":"10.1093","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9506-4047","authenticated-orcid":false,"given":"Bonnie E","family":"Shook-Sa","sequence":"first","affiliation":[{"name":"University of North Carolina at Chapel Hill Department of Biostatistics, , Chapel Hill, NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul N","family":"Zivich","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill Department of 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