Estimation of mean treatment effect is an important tool for evaluating economic policy. The main difficulty in this calculation is caused by nonrandom assignment of potential participants to treatment, which leads to selection bias when ignored. A solution to this problem is to suppose the econometrician observes a set of covariates that determine participation, except for a strictly random component. Under this assumption, known as Ignorability, semiparametric methods were developed, including imputation of counterfactual outcomes and sample reweighing. Both are consistent and can asymptotically achieve the semiparametric efficiency bound. However, in sample sizes commonly available, their performance is not always satisfactory. The goal of this dissertation is to study how combining these strategies can lead to better estimation in small samples. We consider two different ways of merging these methods, based on Doubly Robust inference literature developed by James Robins and his co-authors, analyze their properties and discuss why they would overcome each of their components. Finally, we compare the proposed estimators to imputation and reweighing in a Monte Carlo exercise. Results show that while combined strategies may reduce bias and variance, it depends on the way it is implemented. We conclude that the choice of smoothness parameters is critical to obtain good estimates in moderate size samples.