Estimations of the Conditional Tail Average Treatment Effect

44 Pages Posted: 3 Feb 2021 Last revised: 22 Sep 2021

See all articles by Le‐Yu Chen

Le‐Yu Chen

Academia Sinica

Yu-Min Yen

Department of International Business, National Chengchi University

Date Written: December 1, 2020

Abstract

We study estimation of the conditional tail average treatment effect (CTATE), defined as a difference between conditional tail expectations of potential outcomes. The CTATE can capture heterogeneity and deliver aggregated local information of treatment effects over different quantile levels, and is closely related to the notion of second order stochastic dominance and the Lorenz curve. These properties render it a valuable tool for policy evaluations. We consider a semiparametric treatment effect framework under endogeneity for the CTATE estimation using a newly introduced class of consistent loss functions jointly for the conditioanl tail expectation and quantile. We establish asymptotic theory of our proposed CTATE estimator and provide an efficient algorithm for its implementation. We then apply the method to the evaluation of effects from participating in programs of the Job Training Partnership Act in the US.

Keywords: Causal inference, Conditional tail expectation, Endogeneity, Semiparametric estimation, Treatment effect

JEL Classification: C13, C14, C21

Suggested Citation

Chen, Le‐Yu and Yen, Yu-Min, Estimations of the Conditional Tail Average Treatment Effect (December 1, 2020). Available at SSRN: https://ssrn.com/abstract=3740489 or http://dx.doi.org/10.2139/ssrn.3740489

Le‐Yu Chen

Academia Sinica ( email )

Nankang
Taipei, 11529
Taiwan

Yu-Min Yen (Contact Author)

Department of International Business, National Chengchi University ( email )

64, Section 2, Zhi-nan Road
Wenshan
Taipei, 116
Taiwan

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
38
Abstract Views
293
PlumX Metrics