Estimating outcome distributions for compliers in instrumental variables models
Estimating outcome distributions for compliers in instrumental variables models
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DOI：
10.2307/2971731
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发表时间：
1997-10-01
影响因子：
5.8
通讯作者：
Rubin, DB
中科院分区：
文献类型：
--
作者：
Imbens, GW;Rubin, DB
在Imbens和Ingrist(1994)，Angrist，Imbens和Rubin(1996)和Imbens和Rubin(1997)中，已经概述了假设，在这些假设下，工具变量stimand可以被给予因果解释为局部平均治疗效果，而不需要函数形式或恒定的治疗效果假设。我们扩展了这些结果，表明在这些假设下，人们可以从数据中估计出比编译人员亚群体的平均因果效应更多的结果；原则上，人们可以估计这个亚群体在不同处理下结果的整个边际分布。对于政策制定者来说，这些分布可能是有用的，他们在考虑一个职业培训计划与另一个职业培训计划的优点时，不仅希望考虑平均收入的差异。我们还表明，标准工具变量估计隐含地估计这些潜在的结果分布，而不对这些隐含密度估计施加所需的非负性，并且施加非负性可以显著改变对局部平均治疗效果的估计。我们通过对高中教育回报的分析来说明这些观点--以出生四分之一为工具的高中教育。我们表明，标准的工具变量估计隐含地估计结果分布在很大范围内是负的，并且当我们以任何一种方式施加非负性时，局部平均治疗效果的估计发生了很大的变化。
In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. We extend these results by showing that under these assumptions one can estimate more from the data than the average causal effect for the subpopulation of compliers; one can, in principle, estimate the entire marginal distribution of the outcome under different treatments for this subpopulation. These distributions might be useful for a policy maker who wishes to take into account not only differences in average of earnings when contemplating the merits of one job training programme vs. another. We also show that the standard instrumental variables estimator implicitly estimates these underlying outcome distributions without imposing the required nonnegativity on these implicit density estimates, and that imposing nonnegativity can substantially alter the estimates of the local average treatment effect. We illustrate these points by presenting an analysis of the returns-to a high school education using quarter of birth as an instrument. We show that the standard instrumental variables estimates implicitly estimate the outcome distributions to be negative over a substantial range, and that the estimates of the local average treatment effect change considerably when we impose nonnegativity in any of a variety of ways.