Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis helps us determine how robust our conclusions are when we alter these underlying assumptions. Existing frameworks for sensitivity analysis are concerned with worst-case changes in assumptions. In this work, we argue that using such pessimistic criteria can often become uninformative or lead to conclusions contradicting our prior knowledge about the world. To demonstrate this claim, we generalize the recent s-value framework (Gupta & Rothenhäusler, 2023) to estimate the sensitivity of three different common assumptions in causal inference. Empirically, we find that, indeed, worst-case conclusions about sensitivity can rely on unrealistic changes in the data-generating process. To overcome this, we extend the s-value framework with a new sensitivity analysis criterion: Bayesian Sensitivity Value (BSV), which computes the expected sensitivity of an estimate to assumption violations under priors constructed from real-world evidence. We use Monte Carlo approximations to estimate this quantity and illustrate its applicability in an observational study on the effect of diabetes treatments on weight loss.

翻译：因果推断，尤其是在观察性研究中，依赖于关于真实数据生成过程的不可检验假设。敏感性分析有助于我们确定当改变这些基本假设时，结论的稳健性如何。现有的敏感性分析框架关注的是假设的最坏情况变化。本文认为，采用这种悲观标准往往可能变得无信息，或导致与我们对世界的先验知识相矛盾的结论。为证明这一观点，我们将近期提出的 s-value 框架（Gupta & Rothenhäusler, 2023）泛化，以估计因果推断中三种常见假设的敏感性。实验发现，确实，关于敏感性的最坏情况结论可能依赖于数据生成过程中不切实际的变化。为克服这一问题，我们用一种新的敏感性分析标准——贝叶斯敏感性值（Bayesian Sensitivity Value, BSV）——扩展了 s-value 框架，该标准在基于现实世界证据构建的先验下，计算估计量对假设违反的期望敏感性。我们使用蒙特卡洛近似来估计该量，并在关于糖尿病治疗对体重减轻效果的观察性研究中展示了其适用性。