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。在此类场景中，常采用两种主要方法：基于混杂因素调整的因果推断（CAC）和基于工具变量的因果分析（IVAC）。然而，两种方法均依赖于无法检验的假设，故而在特定假设违背情形下，哪种方法在减轻ACE估计不一致性方面更具优势可能尚不明确。尽管存在通用准则，但关于CAC与IVAC假设之间权衡的直接理论比较仍较为有限。本研究采用普通最小二乘法（OLS）进行CAC分析、两阶段最小二乘法（2SLS）进行IVAC分析，系统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，并探讨了实践中的经验法则。此外，我们提出一套敏感性分析框架，以指导研究者针对特定数据集判断哪种方法可能产生更小的ACE估计不一致性。通过仿真实验及一项妊娠期母亲压力对新生儿出生体重影响的实证案例验证了研究结论。本文讨论了研究结果对因果推断实践的意义，为研究者在特定情境下判断CAC或IVAC的适用性提供依据。