Adjusting for Population Differences Using Machine Learning Methods
提出用机器学习估计干扰函数并融入双重稳健估计器，以调整研究人群与目标人群的差异，得到一致且渐近有效的非参数估计，并用心脏病数据验证。
Abstract The use of real-world data for medical treatment evaluation frequently requires adjusting for population differences. We consider this problem in the context of estimating mean outcomes and treatment differences in a well-defined target population, using clinical data from a study population that overlaps with but differs from the target population in terms of patient characteristics. The current literature on this subject includes a variety of statistical methods, which generally require correct specification of at least one parametric regression model. In this article, we propose to use machine learning methods to estimate nuisance functions and incorporate the machine learning estimates into existing doubly robust estimators. This leads to nonparametric estimators that are n-consistent, asymptotically normal and asymptotically efficient under general conditions. Simulation results demonstrate that the proposed methods perform reasonably well in realistic settings. The methods are illustrated with a cardiology example concerning aortic stenosis.