To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation (TMLE) and double machine learning (DML). To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment (MCI). We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.

翻译：为估计因果效应，从事健康领域观察性研究的分析人员会采用多种策略来减轻由指征混杂导致的偏倚。解决这类问题主要有两大类方法：使用混杂因素和工具变量（IVs）。由于这些方法在很大程度上依赖于不可检验的假设，分析人员必须基于这些方法无法完美运作的不确定范式开展工作。在本教程中，我们系统阐述了在假设可能不成立时，运用两类方法估计因果效应所需遵循的一套通用原则与启发式策略。其关键在于将观察性研究过程重新定义为：假设可能存在某一类方法估计结果的不一致性低于另一类方法的情景。尽管我们讨论的方法论主要聚焦于线性场景，但也会涉及非线性场景的复杂性以及诸如目标最小损失估计（TMLE）和双机器学习（DML）等灵活程序。为演示这些原则的应用，我们研究了多奈哌齐超说明书用于轻度认知障碍（MCI）的案例。我们在分析中比较了传统与灵活方法（基于混杂因素和IV方法）所得结果，并与其相似观察性研究和临床试验结果进行了对比。