Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence
比较了倾向得分匹配与协变量匹配估计量，提出两种新匹配指标，并通过蒙特卡洛实验研究小样本性质，为实践者选择匹配方法提供指导。
We compare propensity-score matching methods with covariatematching estimators. We first discuss the data requirements of propensity-score matching estimators and covariate matching estimators. Then we propose two new matching metrics incorporating the treatment outcome information and participation indicator information, and discuss the motivations of different metrics. Next we study the small-sample properties of propensity-score matching versus covariate matching estimators, and of different matching metrics, through Monte Carlo experiments. Through a series of simulations, we provide some guidance to practitioners on how to choose among different matching estimators and matching metrics. 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.