Matching Methods in Practice: Three Examples

Arbeitspapier

There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical practice where many researchers continue to use simple methods such as ordinary least squares regression even in settings where those methods do not have attractive properties. In this paper I discuss some of the lessons for practice from the theoretical literature, and provide detailed recommendations on what to do. I illustrate the recommendations with three detailed applications.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 8049

Classification and Topics

• Classification: Wirtschaft ; Econometrics ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Model Evaluation, Validation, and Selection
• Subject: matching methods ; propensity score methods ; causality ; unconfoundedness ; potential outcomes ; selection on observables

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Imbens, Guido W.
• Event: Veröffentlichung
• (who): Institute for the Study of Labor (IZA)
• (where): Bonn
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/96693