Sensitivity Analysis for Unmeasured Confounding via Effect Extrapolation
通过效应外推分析未测量混杂的敏感性AI 解读 ↓
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通过效应外推分析未测量混杂的敏感性
- 研究问题
- 观察性研究中存在未测量混杂时，如何利用已测混杂变量下的效应稳定性，评估因果结论对遗漏混杂的敏感程度？
- 方法与思路
- 作者先近似混杂变量的记录过程，比较不同已测混杂子集下的效应，再外推加入假想未测混杂后的调整效应。
- 材料陈述的贡献
- 摘要提出利用大量已测混杂开展敏感性分析的策略，以模拟比较现有方法，并用两个公开因果推断数据集展示应用。
- 阅读关注点
- 应核对混杂记录机制假设、变量子集选择、外推函数与不确定性计算，以及何种遗漏混杂会使外推结果失效。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；外推依赖记录机制假设，摘要未说明其形式和可检验性。
原文摘要
Inferring the causal effect of a non-randomly assigned exposure on an outcome requires adjusting for common causes of the exposure and outcome to avoid biased conclusions. Notwithstanding the efforts investigators routinely make to measure and adjust for such common causes (or confounders), some confounders typically remain unmeasured, raising the prospect of biased inference in observational studies. Therefore, it is crucial that investigators can practically assess their substantive conclusions' relative (in)sensitivity to potential unmeasured confounding. In this article, we propose a sensitivity analysis strategy that is informed by the stability of the exposure effect over different, well-chosen subsets of the measured confounders. The proposal entails first approximating the process for recording confounders to learn about how the effect is potentially affected by varying amounts of unmeasured confounding, then extrapolating to the effect had hypothetical unmeasured confounders been additionally adjusted for. A large set of measured confounders can thus be exploited to provide insight into the likely presence of unmeasured confounding bias, albeit under an assumption about how data on the confounders are recorded. The proposal's ability to reveal the true effect and ensure valid inference after extrapolation is empirically compared with existing methods using simulation studies. We demonstrate the procedure using two different publicly available datasets commonly used for causal inference.