Inverse Confounding Analysis: An Exact Method for Quantifying the Significance of Confounding
逆向混杂分析：精确量化混杂影响AI 解读 ↓
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逆向混杂分析：精确量化混杂影响
- 研究问题
- 未测混杂可能扭曲观察数据中的暴露效应；在给定频率和成对关联后，如何刻画所有相容联合分布中的风险比完整范围？
- 方法与思路
- 作者将相容联合分布重建写成非线性方程，得到单一自由参数的线性参数化，再把分层风险比表达为该参数的分式线性函数。
- 材料陈述的贡献
- 摘要称 ICA 将 E-value 的最坏情形下界扩展为可容许配置中的完整解析范围，但需要额外输入暴露、混杂因素与结局频率。
- 阅读关注点
- 应核对变量与输入参数的定义、相容分布的非负性约束，以及分层风险比和目标因果效应之间还需要哪些假设。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；精确性针对给定输入的相容集合，摘要未说明未知混杂参数如何获得。
原文摘要
The presence of unmeasured confounding factors during the collection of observational data may lead to biased estimates of the effect of an exposure on an outcome. Consequently, a central problem in causal inference based on observational data is sensitivity analysis with respect to unmeasured confounding. Existing sensitivity analyses generally focus on worst-case bounds. We propose an exact method for quantifying the significance of confounding, defined here in terms of the complete range of analytical estimates of the stratification-based Risk Ratio over the set of all joint distributions compatible with the observed characteristics. We refer to the proposed method as Inverse Confounding Analysis (ICA). The proposed ICA method extends the widely used E-value approach but, in contrast to it, does not restrict the analysis to a worst-case lower bound. Instead, it provides exact estimates over the entire set of admissible configurations. This requires several additional input parameters, namely the frequencies of the exposure, the confounder, and the outcome. The ICA method is based on an inverse problem: reconstructing the set of admissible joint distributions from specified frequencies and pairwise associations. We formulate this reconstruction problem as a system of nonlinear equations and obtain an analytical solution. Surprisingly, the complete solution set can be parameterized linearly by a single free parameter. The corresponding stratification-based Risk Ratio is then represented as a fractional-linear function of this parameter. This representation makes it possible to derive exact analytical measures of the significance of confounding over the entire set of admissible statistical configurations.