Combining Strategies for the Estimation of Treatment Effects

The estimation of the average effect of a program or treatment on a variable of interest is an important tool for the assessment of economic policies. In general, assignment of potential participants to treatment does not occur at random and could thus generate a selection bias in absence of some correction. A way to get around this problem is by assuming that the econometrician observes a set of determinant characteristics of participation up to a strictly random component. Under such an assumption, the literature contains semiparametric estimators of the average treatment effect that are consistent and can asymptotically reach the semiparametric efficiency bound. However, in frequently available samples, the performance of these methods is not always satisfactory.The aim of this paper is to investigate how the combination of two strategies may generate estimators with better properties in small samples. Therefore, we consider two ways of combining these approaches, based on the double robustness literature developed by James Robins et al. We analyze the properties of these combined estimators and discuss why they can outperform the separate use of each method. Finally, using a Monte Carlo simulation, we compare the performance of these estimators with that of the imputation and reweighting techniques. Our results show that the combination of strategies can reduce bias and variance, but this improvement depends on adequate implementation. We conclude that the choice of smoothing parameters is decisive for the performance of estimators in medium-sized samples.