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The performance of estimators based on the propensity score

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.
   
type journal paper
   
keywords Propensity score matching, kernel matching, inverse probability weighting, inverse probability tilting, selection on observables, empirical Monte Carlo study, finite sample properties.
   
language English
kind of paper journal article
date of appearance 27-2-2013
journal Journal of Econometrics
publisher Elsevier (Amsterdam)
ISSN 0304-4076
ISSN (online) 1872-6895
DOI 10.1016/j.jeconom.2012.11.006
volume of journal 175
number of issue 1
page(s) 1-21
review double-blind review
   
profile area SEPS - Quantitative Economic Methods
citation Huber, M., Lechner, M., & Wunsch, C. (2013). The performance of estimators based on the propensity score. Journal of Econometrics, 175(1), 1-21, DOI:10.1016/j.jeconom.2012.11.006.