On the estimation and interpretation of effect size metrics
效应量指标的估计与解释AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接讨论干预或暴露效应、混杂调整及效应量的因果解释，因此归入因果推断。
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效应量指标的估计与解释
- 研究问题
- 在处理或暴露、观测混杂因素与结果的三方关系中，常用效应量能否区分不同层面的响应，并实现可解释的组间比较？
- 方法与思路
- 作者运用组合数学与信息论所启发的混杂处理技术，评估逻辑回归和 Mantel–Haenszel 估计对响应区分及混杂分布对称性的处理。
- 材料陈述的贡献
- 摘要认为直接使用逻辑回归未恢复所需结构对称性，而 Mantel–Haenszel 估计难以分离三方与两方效应，影响汇总指标解释。
- 阅读关注点
- 应核对两方和三方效应、结构对称性的定义，区分观测混杂平衡与完整因果识别，并检验对两种常用方法的批评适用哪些设定。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或被期刊接收；摘要的混杂消除与因果解释主张不能据此推广至未观测混杂。
原文摘要
Effect size estimates are thought to capture the collective, two-way response to an intervention or exposure in a three-way problem among the intervention/exposure, various confounders and the outcome. For meaningful causal inference from the estimated effect size, the joint distribution of observed confounders must be identical across all intervention/exposure groups. However, real-world observational studies and even randomized clinical trials often lack such structural symmetry. To address this issue, various methods have been proposed and widely utilized. Recently, elementary combinatorics and information theory have motivated a consistent way to completely eliminate observed confounding in any given study. In this work, we leverage these new techniques to evaluate conventional methods based on their ability to (a) consistently differentiate between collective and individual responses to intervention/exposure and (b) establish the desired structural parity for sensible effect size estimation. Our findings reveal that a straightforward application of logistic regression homogenizes the three-way stratified analysis, but fails to restore structural symmetry leaving in particular the two-way effect size estimate unadjusted. Conversely, the Mantel-Haenszel estimator struggles to separate three-way effects from the two-way effect of intervention/exposure, leading to inconsistencies in interpreting pooled estimates as two-way risk metrics.