Estimations of the Conditional Tail Average Treatment Effect
44 Pages Posted: 3 Feb 2021 Last revised: 22 Sep 2021
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: Suggested Citation