Estimations of the Local Conditional Tail Average Treatment Effect

TL;DR

This paper introduces a new method for estimating the local conditional tail average treatment effect (LCTATE) in the presence of noncompliance, providing a semiparametric approach with theoretical guarantees and practical application to policy evaluation.

Contribution

It proposes a novel semiparametric estimator for LCTATE under endogeneity, with asymptotic theory and an efficient algorithm, applied to real policy data.

Findings

The estimator is consistent and asymptotically normal.

Application to US Job Training data demonstrates practical utility.

Provides a new tool for policy analysis with heterogeneity in treatment effects.

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.