In observational studies, unobserved confounding is a major barrier in isolating the average causal effect (ACE). In these scenarios, two main approaches are often used: confounder adjustment for causality (CAC) and instrumental variable analysis for causation (IVAC). Nevertheless, both are subject to untestable assumptions and, therefore, it may be unclear which assumption violation scenarios one method is superior in terms of mitigating inconsistency for the ACE. Although general guidelines exist, direct theoretical comparisons of the trade-offs between CAC and the IVAC assumptions are limited. Using ordinary least squares (OLS) for CAC and two-stage least squares (2SLS) for IVAC, we analytically compare the relative inconsistency for the ACE of each approach under a variety of assumption violation scenarios and discuss rules of thumb for practice. Additionally, a sensitivity framework is proposed to guide analysts in determining which approach may result in less inconsistency for estimating the ACE with a given dataset. We demonstrate our findings both through simulation and an application examining whether maternal stress during pregnancy affects a neonate's birthweight. The implications of our findings for causal inference practice are discussed, providing guidance for analysts for judging whether CAC or IVAC may be more appropriate for a given situation.

翻译：在观察性研究中，未观测混杂是分离平均因果效应（ACE）的主要障碍。此类场景下常采用两种主要方法：因果混杂调整（CAC）和工具变量因果分析（IVAC）。然而，两种方法均依赖于不可检验的假设，因此在特定的假设违背场景下，何种方法在降低ACE不一致性方面更具优势尚不明确。尽管存在通用指南，但关于CAC与IVAC假设权衡的直接理论比较仍有限。本文采用普通最小二乘法（OLS）实施CAC、两阶段最小二乘法（2SLS）实施IVAC，分析比较了多种假设违背场景下两种方法对ACE的相对不一致性，并讨论了实践中的经验法则。此外，我们提出一个敏感性分析框架，以指导分析人员根据给定数据集判断哪种方法能更有效降低ACE估计的不一致性。通过模拟研究及"孕期母亲压力是否影响新生儿出生体重"的实证案例验证了研究结论。最后讨论了这些发现对因果推断实践的启示，为分析人员在特定情境下判断CAC或IVAC的适用性提供了指引。