Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability assumption (also known as unconfoundedness or exogeneity). In practice, however, this assumption 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. Over the past decades, 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, sometimes 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. In this review, after introducing sensitivity analyses for IPW estimators, we focus on different strategies to estimate or lower bound the confounding strength, select the most suitable approach, and avoid common pitfalls in the interpretation of results.

翻译：因果推断仅在其基本假设满足时才有效，其中最核心的假设之一是可忽略性假设（亦称无混杂性或外生性）。然而在实践中，由于某些混杂变量可能未被观测，这一假设在观察性研究中往往不切实际。为应对此局限，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松弛。过去数十年间，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敏感性参数（有时称为“混杂强度”）的设定，该参数量化了偏离可忽略性的程度。然而，为此参数确定合适取值具有挑战性，且敏感性分析的最终解读可能模糊不清。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。本综述在介绍IPW估计量的敏感性分析后，重点探讨估计或下界混杂强度的不同策略，选择最适宜的方法，并避免结果解读中的常见误区。