Double Robust Variance Estimation
arxiv(2024)
摘要
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 exposure model is correctly specified. However, the
influence function based variance estimator frequently used with doubly robust
estimators is only consistent when both the outcome and exposure models are
correctly specified. Here, use of M-estimation and the empirical sandwich
variance estimator for doubly robust point and variance estimation is
demonstrated. Simulation studies illustrate the properties of the influence
function based and empirical sandwich variance estimators. Estimators are
applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP)
trial to estimate the effect of maternal anemia on birth weight among women
with HIV. In the example, birth weights if all women had anemia were estimated
to be lower than birth weights if no women had anemia, though estimates were
imprecise. Variance estimates were more stable under varying model
specifications for the empirical sandwich variance estimator than the influence
function based variance estimator.
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