Some Practical Guidance for the Implementation of Propensity Score Matching

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: DIW Discussion Papers ; No. 485

Classification and Topics

• Classification: Wirtschaft ; Project Evaluation; Social Discount Rate ; Econometric and Statistical Methods: Special Topics: General
• Subject: Propensity Score Matching ; Implementation ; Evaluation ; Sensitivity

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Caliendo, Marco ; Kopeinig, Sabine
• Event: Veröffentlichung
• (who): Deutsches Institut für Wirtschaftsforschung (DIW)
• (where): Berlin
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/18336
• Last update: 10.03.2025, 11:42 AM CET