Estimations of the Local Conditional Tail Average Treatment Effect

Le-Yu Chen and Yu-Min Yen

Papers from arXiv.org

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 paper, 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 conditional tail expectation 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 US.

Date: 2021-09, Revised 2024-05
New Economics Papers: this item is included in nep-ecm and nep-isf
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Citations: View citations in EconPapers (1)

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Journal Article: Estimation of the Local Conditional Tail Average Treatment Effect (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2109.08793

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