Efficient semiparametric estimation of multi-valued treatment effects under ignorability
Efficient semiparametric estimation of multi-valued treatment effects under ignorability
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DOI：
10.1016/j.jeconom.2009.09.023
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发表时间：
2010-04-01
影响因子：
6.3
通讯作者：
Cattaneo, Matias D.
中科院分区：
文献类型：
--
作者：
Cattaneo, Matias D.
本文研究了一个大类的多值治疗效果的有效估计隐含地定义了一个集合的可能过度识别的非光滑矩条件时，治疗分配被假定为可验证的。引入了两个估计，并给出了一组充分条件，保证了它们的根n-相合性、渐近正态性和有效性。在温和的假设下，这些条件满足边际平均治疗效果和边际分位数治疗效果，实证应用特别重要的估计。当处理是二分的时，本文提出的方法涵盖了平均和分位数处理效应的先前结果。结果表明，经验性的应用研究母亲吸烟强度在怀孕期间对出生体重的影响，和蒙特卡洛实验。(c)2009爱思唯尔有限公司版权所有。
This paper studies the efficient estimation of a large class of multi-valued treatment effects as implicitly defined by a collection of possibly over-identified non-smooth moment conditions when the treatment assignment is assumed to be ignorable. Two estimators are introduced together with a set of sufficient conditions that ensure their root n-consistency, asymptotic normality and efficiency. Under mild assumptions, these conditions are satisfied for the Marginal Mean Treatment Effect and the Marginal Quantile Treatment Effect, estimands of particular importance for empirical applications. Previous results for average and quantile treatments effects are encompassed by the methods proposed here when the treatment is dichotomous. The results are illustrated by an empirical application studying the effect of maternal smoking intensity during pregnancy on birth weight, and a Monte Carlo experiment. (c) 2009 Elsevier B.V. All rights reserved.