Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators
分析了匹配法和加权法在估计平均处理效应时的有限样本表现，发现岭匹配比配对匹配均方误差小约25%，且对设计选择最不敏感。
The finite-sample properties of matching and weighting estimators, often used for estimating average treatment effects, are analyzed. Potential and feasible precision gains relative to pair matching are examined. Local linear matching (with and without trimming), k-nearest-neighbor matching, and particularly the weighting estimators performed worst. Ridge matching, on the other hand, leads to an approximately 25% smaller MSE than does pair matching. In addition, ridge matching is least sensitive to the design choice. 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.