Estimation of Average Treatment Effects With Misclassification
Estimation of Average Treatment Effects With Misclassification
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DOI：
10.1111/j.1468-0262.2006.00756.x
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发表时间：
2007-03
期刊：
影响因子：
6.1
通讯作者：
Arthur Lewbel
中科院分区：
文献类型：
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作者：
Arthur Lewbel
本文考虑识别和估计的边际效应的一个被误测的二元回归在非参数回归，或有条件的平均效应的二元治疗或政策的一些结果，治疗可能被错误分类。误分类概率和治疗的真实概率也是非参数识别的。分类错误发生在计量治疗时有误差，即有些单位报告说得到了治疗，而实际上没有得到治疗，反之亦然。识别假设是存在一个影响治疗决策的变量（二元回归变量），并满足一些条件独立性假设。这个变量可能是一个工具或治疗的第二个错误测量。估计可以是普通的GMM或建议的局部GMM，它通常可以用于基于条件矩限制的非参数估计函数。提供了一个估计学校教育回报的经验应用程序。
This paper considers identification and estimation of the marginal effect of a mismeasured binary regressor in a nonparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Misclassification probabilities and the true probability of treatment are also nonparametrically identified. Misclassification occurs when treatment is measured with error, that is, some units are reported to have received treatment when they actually have not, and vice versa. The identifying assumption is existence of a variable that affects the decision to treat (the binary regressor) and satisfies some conditional independence assumptions. This variable could be an instrument or a second mismeasure of treatment. Estimation is either ordinary GMM or a proposed local GMM, which can be used generally to nonparametrically estimate functions based on conditional moment restrictions. An empirical application estimating returns to schooling is provided.