Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesis is often unrealistic in observational studies, as some confounding variables may remain unobserved. To address this limitation, sensitivity models for Inverse Probability Weighting (IPW) estimators, known as Marginal Sensitivity Models, have been introduced, allowing for a controlled relaxation of ignorability. A substantial body of literature has emerged around these models, aiming to derive sharp and robust bounds for both binary and continuous treatment effects. A key element of these approaches is the specification of a sensitivity parameter, referred to as the "confounding strength", which quantifies the extent of deviation from ignorability. Yet, determining an appropriate value for this parameter is challenging, and the final interpretation of sensitivity analyses can be unclear. We believe these difficulties represent major obstacles to the adoption of such methods in practice. Therefore, after introducing sensitivity analyses for IPW estimators, we review different strategies to estimate or lower bound the confounding strength, introduce a new method leveraging negative controls, provide a decision tree with guidelines to choose a suitable approach, and compare the methodologies in an in-depth simulation study.

翻译：因果推断仅在其基本假设成立时有效，其中最核心的假设之一是可忽略性或无混杂性假设。然而，在观察性研究中，这一假设往往不切实际，因为某些混杂变量可能未被观测到。为应对这一局限，针对逆概率加权（IPW）估计量的敏感性模型——即边际敏感性模型——被提出，允许对可忽略性进行可控的放松。围绕这些模型已涌现大量文献，旨在为二元及连续处理效应推导出尖锐且稳健的边界。这些方法的关键要素在于设定一个称为“混杂强度”的敏感性参数，该参数量化了偏离可忽略性的程度。然而，如何确定该参数的适当取值具有挑战性，且敏感性分析的最终解释可能不够清晰。我们认为这些困难是此类方法在实践中推广应用的主要障碍。因此，在介绍IPW估计量的敏感性分析后，我们综述了估计或下界混杂强度的不同策略，提出一种利用阴性对照的新方法，提供包含选用合适方法指导原则的决策树，并通过深入的模拟研究对各方法进行比较。