The performance of estimators based on the propensity score

Martin Huber, Michael Lechner and Conny Wunsch

Journal of Econometrics, 2013, vol. 175, issue 1, 1-21

Abstract: We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters are important for all estimators. A conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score and different types of outcome variables is considered an important property.

Keywords: Propensity score matching; Kernel matching; Inverse probability weighting; Inverse probability tilting; Selection on observables; Empirical Monte Carlo study; Finite sample properties (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (300)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:175:y:2013:i:1:p:1-21

DOI: 10.1016/j.jeconom.2012.11.006

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