Estimation of Average Treatment Effects with Misclassification
研究当二元处理变量存在误分类时，如何识别和估计其平均处理效应，发现忽略误分类会导致估计偏误，并证明存在一个条件独立的工具变量即可实现非参数识别。
This paper considers identification and estimation of the effect of a mismeasured binary regressor in a nonparametric or semiparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Failure to account for misclassification is shown to result in attenuation bias in the estimated treatment effect. An identifying assumption that overcomes this bias is the existence of an instrument for the binary regressor that is conditionally independent of the treatment effect. A discrete instrument suffices for nonparametric identification. Copyright The Econometric Society 2007.