Frameworks for Estimating Causal Effects in Observational Settings: Comparing Confounder Adjustment and Instrumental Variables
观察性研究中的因果效应估计框架：混杂调整与工具变量比较
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…