Stuart Elizabeth A
Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, Department of Biostatistics, 624 N Broadway, 8th Floor, Baltimore, MD 21205.
Stat Sci. 2010 Feb 1;25(1):1-21. doi: 10.1214/09-STS313.
When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
在使用观察性数据估计因果效应时，希望通过获得具有相似协变量分布的处理组和对照组，尽可能紧密地复制随机实验。这个目标通常可以通过从原始处理组和对照组中选择匹配良好的样本实现，从而减少由于协变量导致的偏差。自20世纪70年代以来，关于匹配方法的研究探讨了如何最好地选择处理组和对照组进行比较。匹配方法在经济学、流行病学、医学和政治学等领域越来越受欢迎。然而，到目前为止，相关文献和建议分散在各个学科中。对使用匹配方法或开发与匹配相关方法感兴趣的研究人员没有一个统一的地方来了解过去和当前的研究。本文提供了一个思考匹配方法的框架及其使用指南，整合了现有的研究（包括新旧研究），并总结了匹配方法文献的现状和发展方向。
