Assessing the performance of matching algorithms when selection into treatment is strong
研究匹配方法中距离度量和算法类型的选择，使用NLSY79数据评估大学教育对收入的影响，发现强选择性和处理效应异质性时全匹配更优，异质性弱时配对匹配即可。
Abstract This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong selection into treatment and treatment effect heterogeneity a full matching seems preferable. If heterogeneity is weak, pair matching suffices. Copyright © 2007 John Wiley & Sons, Ltd.