标题： 对未观测混杂因素的敏感性分析：在敏感性模型中估计混杂强度的比较综述 显示英文标题

标题： Sensitivity Analysis to Unobserved Confounders: A Comparative Review to Estimate Confounding Strength in Sensitivity Models

摘要： 因果推断只有在其基本假设得到满足时才是有效的，其中最重要的是不可忽略性假设（也称为无混杂性或外生性）。 然而，在实践中，这一假设在观察性研究中往往不现实，因为可能有一些混杂变量未被观测到。 为解决这一局限性，引入了针对逆概率加权（IPW）估计器的敏感性模型，称为边际敏感性模型，允许对不可忽略性进行受控放松。 在过去几十年中，围绕这些模型出现了大量文献，旨在为二元和连续处理效应推导出精确且稳健的界限。 这些方法的一个关键要素是设定一个敏感性参数，有时被称为“混杂强度”，它量化了与不可忽略性的偏离程度。 然而，确定该参数的适当值具有挑战性，敏感性分析的最终解释可能不明确。 我们认为这些困难是实际采用此类方法的主要障碍。 在本文综述中，介绍了IPW估计器的敏感性分析后，我们重点探讨了估计或下限估计混杂强度的不同策略，选择最合适的的方法，并避免结果解释中的常见陷阱。 显示英文摘要

摘要： 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.