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Title:How to control for many covariates? Reliable estimators based on the propensity score PDF Logo
Authors:Huber, Martin
Lechner, Michael
Wunsch, Conny
Issue Date:2010
Series/Report no.:Discussion paper series // Forschungsinstitut zur Zukunft der Arbeit 5268
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 observable 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 is important for all estimators. The key 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 is considered an important property.
Subjects:propensity score matching
kernel matching
inverse probability weighting
selection on observables
empirical Monte Carlo study
finite sample properties
JEL:C21
Document Type:Working Paper
Appears in Collections:IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)

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