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.

翻译：工具变量回归是观察性数据分析中常用的工具。工具变量用于在存在未测量混杂因素的情况下，对某种暴露的效应进行因果推断。有效的工具变量需满足与暴露相关、仅通过暴露影响结果（排他性准则）且不与结果存在混杂（外生性）的条件。这些假设通常无法直接检验，需依赖领域知识，因此有必要进行敏感性分析以评估假设违反时对估计参数的影响。本文提出并展示了一种针对因果线性及非线性模型中G-估计量的新敏感性分析方法。我们在工具变量研究中引入两个创新点：其一，采用单一敏感性参数捕获排他性和外生性假设的违反；其二，将该方法扩展至非线性模型。所提出的框架具有理论依据，并通过仿真研究进行验证。最后，通过实际数据案例说明该方法的应用，并为实践者提供开展敏感性分析的指导原则。