Shook-Sa Bonnie E, Zivich Paul N, Lee Chanhwa, Xue Keyi, Ross Rachael K, Edwards Jessie K, Stringer Jeffrey S A, Cole Stephen R
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Biometrics. 2025 Apr 2;81(2). doi: 10.1093/biomtc/ujaf054.
Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or an exposure model is correctly specified. 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.
双稳健估计量因其在结果模型或暴露模型正确设定时能够提供一致的点估计，而在因果推断领域受到欢迎。然而，对于非随机暴露，常用于平均因果效应双稳健估计量的基于影响函数的方差估计量仅在两个工作模型（即结果模型和暴露模型）都正确设定时才是一致的。在此，经验三明治方差估计量和非参数自助法被证明是双稳健方差估计量。也就是说，当只有一个工作模型正确设定时，预期它们能提供有效的方差估计，从而得到名义置信区间覆盖率。模拟研究说明了在假设参数化工作模型的情况下，基于影响函数、经验三明治和非参数自助法方差估计量的性质。这些估计量被应用于孕酮改善妊娠结局（IPOP）研究的数据，以估计艾滋病毒感染女性中母亲贫血对出生体重的影响。
