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...

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Main Author: Gabriel V. Montes-Rojas
Format: Article
Language:English
Published: Wiley 2011-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2011/874251
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author Gabriel V. Montes-Rojas
author_facet Gabriel V. Montes-Rojas
author_sort Gabriel V. Montes-Rojas
collection DOAJ
description 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.
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institution Kabale University
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spelling doaj-art-7121917b73284247afdbacdb9124df1b2025-02-03T01:08:00ZengWileyJournal of Probability and Statistics1687-952X1687-95382011-01-01201110.1155/2011/874251874251Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical AnalysisGabriel V. Montes-Rojas0Department of Economics, City University London, D306 Social Sciences Building, Northampton Square, London EC1V 0HB, UKNonparametric 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.http://dx.doi.org/10.1155/2011/874251
spellingShingle Gabriel V. Montes-Rojas
Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
Journal of Probability and Statistics
title Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
title_full Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
title_fullStr Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
title_full_unstemmed Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
title_short Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis
title_sort nonparametric estimation of ate and qte an application of fractile graphical analysis
url http://dx.doi.org/10.1155/2011/874251
work_keys_str_mv AT gabrielvmontesrojas nonparametricestimationofateandqteanapplicationoffractilegraphicalanalysis