SUMMARY Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures for testing the binary instrumental variable model. Our methods are based on existing techniques for comparing two treatments, such as the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$t$\end{document}-test and the Gail–Simon test. We illustrate the importance of testing the instrumental variable model by evaluating the exogeneity of college proximity using the National Longitudinal Survey of Young Men.

中文翻译：

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论二元工具变量模型的证伪

总结 工具变量被广泛用于在存在无法测量的混杂因素的情况下估计因果效应。离散工具变量模型对观测数据的规律具有可检验的含义。然而，目前对工具有效性的评估通常仅基于主题论据而不是这些可测试的含义，部分原因是缺乏具有已知属性的正式统计测试。在本文中，我们开发了用于测试二元工具变量模型的简单程序。我们的方法基于比较两种治疗方法的现有技术，例如 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\ oddsidemargin}{-69pt} \begin{document} }{}$t$\end{document}-test 和 Gail-Simon 测试。我们通过使用全国青年男性纵向调查评估大学邻近性的外生性来说明测试工具变量模型的重要性。