Cao Weihua, Tsiatis Anastasios A, Davidian Marie
Department of Statistics , North Carolina State University , Raleigh, North Carolina 27695-8203 , U.S.A.
Biometrika. 2009 Sep;96(3):723-734. doi: 10.1093/biomet/asp033. Epub 2009 Aug 7.
Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.
最近，相当多的关注集中在存在不完整数据时总体平均响应的双稳健估计量上，这涉及倾向得分模型和结果对协变量的回归模型。如果这两个模型都未正确设定，通常的双稳健估计量可能会产生严重有偏的推断，并且如果某些观测值的估计倾向得分接近零，可能会表现出不可忽略的偏差。我们提出了替代的双稳健估计量，即使某些估计倾向得分接近零，相对于现有方法也能实现相当或更好的性能。
