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，健康领域开展观察性研究的分析人员会采用多种策略来减轻由适应症混杂导致的偏倚。这类方法主要分为两大类：使用混杂因子与工具变量（IV）。由于这些方法在很大程度上依赖于无法检验的假设，分析人员必须在这些方法效果不完美的模糊范式下进行操作。在本教程中，我们系统化了一组通用原则与启发式方法，用于在假设可能被违反时，通过这两种方法估计因果效应。这关键需要将观察性研究的过程重新定义为假设可能场景——即某方法估计结果的不一致性低于另一种方法的情形。虽然我们的方法论讨论主要围绕线性场景展开，但也涉及非线性场景的复杂性及灵活程序，如基于目标最小损失估计（TMLE）与双机器学习（DML）。为演示我们原则的应用，我们研究了多奈哌齐在轻度认知障碍（MCI）中的超说明书使用。通过分析，我们比较并对比了传统与灵活方法中混杂因子与工具变量方法的结果，并与一项类似观察性研究及临床试验进行了对照。