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政大機構典藏 > 商學院 > 國際經營與貿易學系 > 期刊論文 > Item 140.119/152334

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152334

Title:	Estimation of the Local Conditional Tail Average Treatment Effect
Authors:	顏佑銘 Yen, Yu-Min;Chen, Le-Yu
Contributors:	國貿系
Keywords:	Causal inference;Conditional tail expectation;Endogeneity;Semiparametric estimation;Treatment effects
Date:	2024-06
Issue Date:	2024-07-17
Abstract:	The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity and deliver aggregated local information on 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 evaluation. In this article, we study estimation of the CTATE locally for a group of compliers (local CTATE or LCTATE) under the two-sided noncompliance framework. We consider a semiparametric treatment effect framework under endogeneity for the LCTATE estimation using a newly introduced class of consistent loss functions jointly for the CTE and quantile. We establish the asymptotic theory of our proposed LCTATE estimator and provide an efficient algorithm for its implementation. We then apply the method to evaluate the effects of participating in programs under the Job Training Partnership Act in the United States.
Relation:	Journal of Business & Economic Statistics, pp.1-15
Data Type:	article
DOI 連結:	https://doi.org/10.1080/07350015.2024.2356731
DOI:	10.1080/07350015.2024.2356731
Appears in Collections:	[國際經營與貿易學系 ] 期刊論文

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