用真实证据构建先验，更合理评估因果推断的敏感性
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
- 基于真实世界证据构建先验，替代极端假设下的敏感性分析
- 提出贝叶斯敏感性值（BSV），量化假设偏离时估计值的平均变化
- 在糖尿病治疗影响体重的研究中验证了方法的有效性
因果推断在观察性研究中依赖于无法检验的假设。敏感性分析用于评估结论在假设改变时的稳健性。现有框架关注最坏情况下的假设变化，但这类悲观标准常导致无信息结果或与现实知识矛盾。本文将近期提出的s-value框架推广至三种常见因果假设的敏感性分析。实证发现，最坏情况结论往往依赖于不现实的数据生成过程改变。为此，我们引入贝叶斯敏感性值（BSV），在基于真实世界证据构建的先验下，计算估计值对假设违背的期望敏感性。通过蒙特卡洛近似估算该量，并在一项关于糖尿病治疗对体重影响的观察性研究中展示其应用。
原文摘要 · Abstract (English)
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.
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