Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion criterion), and is not confounded with the outcome (exogeneity). These assumptions are generally untestable and rely on subject-matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G-estimators in causal linear and non-linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non-linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real-world data and provide practitioners with guidelines on conducting sensitivity analysis.

中文翻译：

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G-估计量对无效工具变量的敏感性分析

工具变量回归是一种常用于观测数据分析的工具。工具变量用于在存在未测量的混杂因素的情况下对特定暴露的影响进行因果推断。有效的工具变量是与暴露相关的变量，仅通过暴露（排除标准）影响结果，并且与结果不混淆（外生性）。这些假设通常是不可测试的，并且依赖于主题知识。因此，需要进行敏感性分析来评估假设违反对估计参数的影响。在本文中，我们提出并演示了一种在因果线性和非线性模型中对 G 估计量进行敏感性分析的新方法。我们在工具变量研究中介绍了敏感性分析的两个新方面。第一个是单个灵敏度参数，用于捕获违反排除和外生假设的情况。第二个是该方法在非线性模型中的应用。引入的框架在理论上是合理的，并通过模拟研究进行了说明。最后，我们通过应用到现实世界的数据来说明该方法，并为从业者提供进行敏感性分析的指南。