Abstract

This study describes a novel method to reweight a comparison group used for causal inference, so the group is similar to a treatment group on observable characteristics yet avoids highly variable weights that would limit statistical power. The proposed method generalizes the covariate-balancing propensity score (CBPS) methodology developed by Imai and Ratkovic (2014 Imai, K., and Ratkovic, M. (2014),”Covariate Balancing Propensity Score,” Journal of the Royal Statistical Society, Series B, 76, 243–263. DOI: 10.1111/rssb.12027.[Crossref] , [Google Scholar]) to enable researchers to effectively prespecify the variance (or higher-order moments) of the matching weight distribution. This lets researchers choose among alternative sets of matching weights, some of which produce better balance and others of which yield higher statistical power. We demonstrate using simulations that our penalized CBPS approach can improve effect estimates over those from other established propensity score estimation approaches, producing lower mean squared error. We discuss applications where the method or extensions of it are especially likely to improve effect estimates and we provide an empirical example from the evaluation of Comprehensive Primary Care Plus, a U.S. health care model that aims to strengthen primary care across roughly 3000 practices. Programming code is available to implement the method in Stata.

摘要

本研究描述了一种重新加权用于因果推断的对照组的新方法，因此该组与可观察特征的治疗组相似，但避免了会限制统计功效的高度可变的权重。所提出的方法概括了由 Imai 和 Ratkovic ( 2014 ) 开发的协变量平衡倾向评分 (CBPS) 方法 Imai, K.和Ratkovic, M.（2014 年），“协变量平衡倾向评分”，《皇家统计学会杂志》，B 系列，76、243 – 263。DOI：10.1111/rssb.12027。[交叉引用]  , [谷歌学术]) 使研究人员能够有效地预先指定匹配权重分布的方差（或高阶矩）。这让研究人员可以在不同的匹配权重组中进行选择，其中一些会产生更好的平衡，而另一些则会产生更高的统计能力。我们使用模拟证明，我们的惩罚 CBPS 方法可以改善效果估计，而不是其他已建立的倾向评分估计方法，从而产生较低的均方误差。我们讨论了其中的方法或它的扩展特别有可能改善效果估计的应用，并且我们提供了一个来自综合初级保健 Plus 评估的经验示例，这是一种旨在在大约 3000 种实践中加强初级保健的美国医疗保健模型。编程代码可用于在 Stata 中实现该方法。