Data-Adaptive Bias-Reduced Doubly Robust Estimation
Data-Adaptive Bias-Reduced Doubly Robust Estimation
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DOI：
10.1515/ijb-2015-0029
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发表时间：
2016-05-01
影响因子：
1.2
通讯作者：
Vansteelandt, Stijn
中科院分区：
文献类型：
--
作者：
Vermeulen, Karel;Vansteelandt, Stijn
双稳健估计，现在已经提出了各种目标参数的因果推理和缺失数据文献。当两个滋扰工作模型中的一个被正确指定时，这些一致地估计半参数模型下的感兴趣的参数，而不管是哪一个。最近提出的偏差减少双重鲁棒估计程序的目的是部分保留这种鲁棒性在更现实的设置，两个工作模型被误指定。这些所谓的偏置减少双重稳健估计利用特殊的（有限维）滋扰参数估计，旨在局部最小化的平方渐近偏差的双重稳健估计在某些方向上的这些有限维滋扰参数下的两个参数工作模型的误指定。在这篇文章中，我们扩展了这一想法，将使用数据自适应估计（无限维滋扰参数），利用偏差减少估计原则的方向只有一个滋扰参数。我们还提供了一个渐近线性定理，它给出了正确规范的参数滋扰工作模型的缺失机制/倾向得分，但可能错误指定（有限或无限维）的结果工作模型的影响函数的建议双重鲁棒估计。仿真研究证实了所需的有限样本性能的估计相对于其他各种双重鲁棒估计。
Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite-or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.