On propensity score matching with a diverging number of matches
研究了倾向得分匹配中近邻数量随样本量增长时的渐近性质，发现改进的估计量能提高效率，并在特定条件下达到半参效率下界。
Abstract This paper re-examines the work of Abadie & Imbens (2016) on propensity score matching for average treatment effect estimation. We explore the asymptotic behaviour of these estimators when the number of nearest neighbours, M, grows with the sample size. It is shown, while not surprising, but technically nontrivial, that the modified estimators can improve upon the original fixed M-estimators in terms of efficiency. Additionally, we demonstrate the potential to attain the semiparametric efficiency lower bound when the propensity score admits some special structures, echoing the insight of Hahn (1998).