Stress-Testing Assumptions: A Guide to Bayesian Sensitivity Analyses in Causal Inference
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对假设进行压力测试：因果推断中的贝叶斯敏感性分析指南
- 研究问题
- 观察研究的因果识别假设无法验证时，如何用贝叶斯方法表达对假设违背的判断，并评估其对推断的影响？
- 方法与思路
- 作者以统一的贝叶斯缺失数据框架处理暴露误分类、未测混杂和非随机缺失结局，并介绍 Stan 实现。
- 材料陈述的贡献
- 摘要称，文章通过四个示例整合方法、代码与实施说明，帮助读者掌握贝叶斯因果敏感性分析的操作流程。
- 阅读关注点
- 应核对敏感性参数与假设违背的对应关系、先验信息来源，以及后验结论对先验和模型设定的依赖程度。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未给出具体先验和识别条件，敏感性分析本身不能验证假设。
原文摘要
While observational data are routinely used to estimate causal effects of biomedical treatments, doing so requires special methods to adjust for observed confounding. These methods invariably rely on untestable statistical and causal identification assumptions. When these assumptions do not hold, sensitivity analysis methods can be used to characterize how different violations may change our inferences. The Bayesian approach to sensitivity analyses in causal inference has unique advantages as it allows users to encode subjective beliefs about the direction and magnitude of assumption violations via prior distributions and make inferences using the updated posterior. However, uptake of these methods remains low since implementation requires substantial methodological knowledge. Moreover, while implementation with publicly available software is possible, it is not straight-forward. At the same time, there are few papers that provide practical guidance on these fronts. In this paper, we walk through four examples of Bayesian sensitivity analyses: 1) exposure misclassification, 2) unmeasured confounding, and missing not-at-random outcomes with 3) parametric and 4) nonparametric Bayesian models. We show how all of these can be done using a unified Bayesian "missing data" approach. We also cover implementation using Stan, a publicly available open-source software for fitting Bayesian models. To the best of our knowledge, this is the first paper that presents a unified approach with code, examples, and methodology in a three-pronged illustration of sensitivity analyses in Bayesian causal inference. Our goal is for the reader to walk away with implementation-level knowledge.