The estimation of the average treatment effect on the treated (ATT) plays an essential role when the effect of an intervention or a treatment on those participants who actually received it is the focus. However, the validity of conventional estimation methods relies on the precise measurement of variables. Misclassified category outcome variables may cause non-negligible bias when estimating ATT. In this paper, under the assumption that the misclassification probability is homogeneous, we develop a bias-corrected estimation method to consistently estimate ATT when internal validation data are available for a subgroup of the study population. We further derive a doubly robust estimator by augmenting the bias-corrected estimator to provide protection against treatment model misspecification. Through simulation experiments and real data analysis, we demonstrate the satisfactory performance of the proposed estimators.

中文翻译：

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估计对错误分类二元结果治疗的平均治疗效果

当干预或治疗对实际接受治疗的参与者的影响成为焦点时，对被治疗者的平均治疗效果 (ATT) 的估计起着至关重要的作用。然而，传统估计方法的有效性依赖于对变量的精确测量。在估计 ATT 时，错误分类的类别结果变量可能会导致不可忽略的偏差。在本文中，在错误分类概率是同质的假设下，我们开发了一种偏差校正估计方法，以在研究人群的一个子组可获得内部验证数据时一致地估计 ATT。我们通过增加偏差校正的估计量来进一步推导出一个双重稳健的估计量，以防止治疗模型错误指定。通过仿真实验和真实数据分析，