Some practical guidance for the implementation of propensity score matching
Some practical guidance for the implementation of propensity score matching
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DOI：
10.1111/j.1467-6419.2007.00527.x
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发表时间：
2008-02-01
影响因子：
5.3
通讯作者：
Kopeinig, Sabine
中科院分区：
文献类型：
--
作者：
Caliendo, Marco;Kopeinig, Sabine
倾向评分匹配（PSM）已成为估计因果治疗效果的流行方法。在评价劳动力市场政策时，这一方法得到广泛应用，但在各种研究领域都可以找到实证例子。一旦研究人员决定使用PSM，他就面临着许多关于其实现的问题。开始，首先必须做出关于倾向分数的估计的决定。在此之后，必须决定选择哪种匹配算法并确定共同支持的区域。随后，必须评估匹配质量，并估计治疗效果及其标准误。此外，诸如“如果有基于选择的抽样，该怎么办？或“何时测量效果？在实证研究中可能很重要最后，人们可能还想测试估计的治疗效果相对于未观察到的异质性或共同支持条件的失败的敏感性。每个实施步骤都涉及到许多决策，可以考虑不同的方法。本文的目的是讨论这些实施问题，并提供一些指导研究人员谁想要使用PSM的评估目的。
Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like 'what to do if there is choice-based sampling?' or 'when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.