Nonparametric estimation of ATE and QTE: an application of Fractile Graphical Analysis

Nonparametric estimators for average and quantile treatment effects are constructed using Fractile Graphical Analysis, under the identifying assumption that selection to treatment is based on observable characteristics. The proposed method has two-steps: first, the propensity score is estimated, and second, a blocking estimation procedure using this estimate is used to compute treatment effects. In both cases, the estimators are proved to be consistent. Monte Carlo results show a better performance than other procedures based on the propensity score. Finally, these estimators are applied to a job training dataset.