TY  - RPRT
        AU  - Huber, Martin
AU  - Lechner, Michael
AU  - Wunsch, Conny
        TI  - How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score
        PY  - 2010/Oct/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 5268
        UR  - https://www.iza.org/publications/dp5268
        AB  - 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.
        KW  - finite sample properties
KW  - empirical Monte Carlo study
KW  - propensity score matching
KW  - kernel matching
KW  - inverse probability weighting
KW  - selection on observables
        ER  -