Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregular covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent quadruply robust estimator and demonstrate analytically and in large simulation studies that it is more flexible and more efficient than its only proposed alternative. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counselling on alcohol consumption in American adolescents.

翻译：电子健康记录及其他观察性数据来源日益被用于因果推断。利用这些非研究目的的数据进行因果效应估计时，会面临混杂偏倚以及影响推断的不规则协变量驱动观测时间。此前已有研究提出一种针对这些特征的双重加权估计量，该估计量依赖于正确设定用于权重的两个干扰模型。在本研究中，我们提出一种新颖且一致的四重稳健估计量，并通过理论分析和大规模模拟研究表明，该估计量比已有的唯一替代方案更灵活、更高效。我们进一步将其应用于美国的Add Health研究数据，以评估治疗咨询对美国青少年饮酒量的因果效应。