The Central Role of the Propensity Score in Observational Studies for Causal Effects
本文证明调整倾向得分（即给定观测协变量下接受特定处理的条件概率）足以消除所有观测协变量带来的偏差，并介绍了其在匹配、子分类和可视化调整中的应用。
The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.