Nonparametric Estimation of ATE and QTE - An Application of Fractile Graphical Analysis - Discussion Paper Series - No. 10/06

Sep 4, 2026

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Nonparametric estimators for average treatment effects (ATE) and quantile treatment effects (QTE) are developed using Fractile Graphical Analysis, assuming that treatment selection depends only on observable characteristics. The procedure follows a two-step design: estimating the propensity score and applying a blocking estimation method based on that estimate to compute treatment effects. Consistency is established for both estimator stages. Monte Carlo experiments demonstrate improved performance versus alternative propensity-score-based methods, and the framework is applied to a job training dataset.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages47

Reading Time1 h 58 min

AccessFree to download

Introduction

Treatment effects in program evaluation

Nonexperimental identification and selection bias

Propensity score based estimation methods

City Research Online City St George’s, University of London Citation: Montes-Rojas, G. (2010) . Nonparametric estimation of ATE and QTE: an application of Fractile Graphical Analysis (10/06) . London, UK: Department of Economics, City University London. This is the unspecified version of the paper. This version of the publication may differ from the final published version. To cite this item please consult the publisher's version. Permanent repository link: [https://openaccess.city.ac.uk/id/eprint/1486/](https://openaccess.city.ac.uk/id/eprint/1486/) [Copyright and Reuse:](Copyright and Reuse: Copyright and Moral Rights remain with the author)[ Copyright and Moral Rights remain with the author](Copyright and Reuse: Copyright and Moral Rights remain with the author)([s](s)) and/or copyright holders. Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge, unless otherwise indicated, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. For full details of reuse please refer to City Research Online policy. City Research Online: [http://openaccess.city.ac.uk/](http://openaccess.city.ac.uk/ publications@citystgeorges.ac.uk)[ ](http://openaccess.city.ac.uk/ publications@citystgeorges.ac.uk)[ publications@citystgeorges.ac.uk](http://openaccess.city.ac.uk/ publications@citystgeorges.ac.uk) Department of Economics Nonparametric Estimation of ATE and QTE:An Application of Fractile Graphical Analysis Gabriel Montes-Rojas* City University London Department of Economics Discussion Paper Series No. 10/06 * Department of Economics, City University London, Social Sciences Bldg, Northampton Square, London EC1V 0HB, UK. [Email: ](Email: Gabriel.Montes-Rojas.1@city.ac.uk)[ Gabriel.Montes-Rojas.1](Email: Gabriel.Montes-Rojas.1@city.ac.uk)[@city.ac.uk](Email: Gabriel.Montes-Rojas.1@city.ac.uk) Nonparametric Estimation of ATE and QTE : An Application of Fractile Graphical Analysis Gabriel V. Montes-Rojas􀀃 City University London Abstract Nonparametric estimators for average and quantile treatment ef fects 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: 􀀌rst, the propensity score is estimated, and second, a blocking estimation procedure using this estimate is used to compute treatment e􀀋ects. 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. JEL: C14, C31 Keywords: Treatment E􀀋ects, Fractile Graphical Analysis 􀀃 Corresponding Author: Department of Economics, City University London, D306 Social Sciences Bldg, Northampton Square, London EC1V 0HB, UK. Email: [Gabriel.Montes-Rojas.1@city.ac.uk](Gabriel.Montes-Rojas.1@city.ac.uk) 1 Introduction Econometric methods for estimating the e􀀋ects of certain programs (such as job search assistance or classroom teaching programs) has been widely developed since the pioneering work of Ashenfelter (1978), LaLonde (1986) and others. In this case, a treatment refers to a certain program whose bene􀀌tsare potentially obtainable by those selected for participation (treated) and it has no e􀀋ect on a control group (non-treated) . Estimating average treatment e􀀋ects (ATE), which refers to the mean e􀀋ect of the program on a given outcome variable in parametric and nonparametric environments (see Angrist and Krueger, 1999, Imbens, 2004) has been a central issue in the literature. Lehmann (1974) and Docksum (1974) introduced the concept of quantile treatment e􀀋ects (QTE) as the di􀀋erence of the quantiles of the treated and control outcome distribut

FAQs

What is the document’s main objective?

To construct nonparametric estimators for ATE and QTE using Fractile Graphical Analysis and to evaluate their performance and consistency.

How does the proposed method estimate ATE and QTE?

It uses a two-step process: first estimate the propensity score, then perform a blocking estimation procedure based on that estimate to compute treatment effects.

What assumptions are used to identify treatment effects in the paper?

The approach relies on the identifying assumption that selection to treatment is based on observable characteristics, together with a unconfoundedness-style conditional independence condition.

Sep 4, 2026

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