因果调解分析与受暴露影响的调解者结果混因素:关于一般化自然间接效应的定义和识别
Yan-Lin Chen1, Tsung Yu2, Sheng-Hsuan Lin1,3,4,5
1From the Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Epidemiology (Cambridge, Mass.)
|September 29, 2025
概括
因果调解分析现在可以识别间接影响,即使有中间混因素. 新的一般化自然间接效应 (NIE) 措施为复杂的因果途径提供了强大的替代方案.
科学领域:
- 因果推理的原因推理.
- 统计建模 统计建模
- 流行病学 流行病学
背景情况:
- 因果调解分析旨在了解暴露-结果途径.
- 介质混因子 (受暴露影响的介质-结果混因子) 挑战了自然间接效应 (NIE) 的识别.
研究的目的:
- 引入新的间接效应措施,一般化的NIEs,以解决由中间混因素引起的识别问题.
- 在存在这种混因素的情况下,提出有效的调解解释标准.
主要方法:
- 开发一类通用NIEs,包括基于随机干预 (干预间接效应,IIE) 的特定案例.
- 根据有效的调解解释提出的标准来评估这些措施.
- 在各种假设下,将一般化的NIEs与传统的NIE和IIE进行比较.
主要成果:
- 传统的NIE不能与标准非参数结构方程模型 (SEMs) 中的中间混因子识别,具有独立错误.
- 拟议的通用NIEs是可识别的,并且满足有效的调解解释的新标准.
- 干预间接效应 (IIE) 仅在传统假设下不符合这些标准.
结论:
- 一般化的NIEs提供了一个实用和可靠的方法,用于因果调解分析,当中间混因素存在时.
- 与现有方法相比,这些新型措施提供了更广泛的选择范围.
- 通过在复杂场景中进行强有力的调解分析,这些发现推动了该领域的发展.
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