How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score

Martin Huber, Michael Lechner and Conny Wunsch

No 5268, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: finite sample properties; empirical Monte Carlo study; propensity score matching; kernel matching; inverse probability weighting; selection on observables (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 65 pages
Date: 2010-10
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Citations: View citations in EconPapers (30)

Published - published as 'The Performance of Estimators Based on the Propensity Score' in: Journal of Econometrics, 2013, 175 (1), 1-21

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Working Paper: How to control for many covariates? Reliable estimators based on the propensity score (2010)
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