Covariate balancing propensity score
Covariate balancing propensity score
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DOI：
10.1111/rssb.12027
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发表时间：
2014-01-01
影响因子：
5.8
通讯作者：
Ratkovic, Marc
中科院分区：
文献类型：
--
作者：
Imai, Kosuke;Ratkovic, Marc
倾向得分在各种因果推理设置中起着核心作用。特别是，基于估计倾向得分的匹配和加权方法在观测数据分析中变得越来越普遍。尽管这些方法很受欢迎，理论上也很有吸引力，但它们的主要实际困难是必须估计倾向得分。研究人员发现，倾向评分模型的轻微错误说明可能导致估计治疗效果的严重偏差。我们引入了协变量平衡倾向评分（CBPS）方法，该方法在优化协变量平衡的同时对治疗分配进行建模。CBPS利用倾向得分作为协变量平衡得分和治疗分配的条件概率的双重特征。CBPS的估计是在广义矩量法或经验似然框架内完成的。我们发现，CBPS显着改善了倾向得分匹配和加权方法在文献中报道的较差的经验性能。我们还表明，CBPS可以扩展到其他重要的设置，包括估计非二元治疗的广义倾向得分和对目标人群的实验估计的泛化。开源软件可用于实现所提出的方法。
The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. We introduce covariate balancing propensity score (CBPS) methodology, which models treatment assignment while optimizing the covariate balance. The CBPS exploits the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment. The estimation of the CBPS is done within the generalized method-of-moments or empirical likelihood framework. We find that the CBPS dramatically improves the poor empirical performance of propensity score matching and weighting methods reported in the literature. We also show that the CBPS can be extended to other important settings, including the estimation of the generalized propensity score for non-binary treatments and the generalization of experimental estimates to a target population. Open source software is available for implementing the methods proposed.