How To Control For Many Covariates? Reliable Estimators Based On The Propensity Score
The authors 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. They 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.