Detecting and Understanding the Difference between Natural Mediation Effects and Their Randomized Interventional Analogues
自然中介效应与随机干预类比量差异的检测与理解AI 解读 ↓
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自然中介效应与随机干预类比量差异的检测与理解
- 研究问题
- 存在处理诱导的混杂时，何时随机干预类比量能代表自然中介效应，能否在较弱条件下检测两者的差异？
- 方法与思路
- 作者提出在足以识别随机干预类比量的假设下检测差异的检验，并从协方差及结构方程角度分析两类效应。
- 材料陈述的贡献
- 摘要称给出首个此类经验检验，并揭示与工具变量估计目标及 Wilcoxon–Mann–Whitney 检验的联系。
- 阅读关注点
- 应核对检验的零假设、可识别的检验对象、效应差异的理论条件，以及不拒绝零假设能支持何种解释。
- 阅读边界
- 这是预印本，同行评审及接收未核实。摘要未给检验条件或功效；未检测到差异不能据此确认两类效应相同。
原文摘要
In causal mediation analysis, the natural direct and indirect effects (natural effects) are nonparametrically unidentifiable in the presence of treatment-induced confounding, which motivated the development of randomized interventional analogues (RIAs) of the natural effects. Being easier to identify, the RIAs are becoming widely used in practice. However, applied researchers often interpret RIA estimates as if they were the natural effects, even though the RIAs can be poor proxies for the natural effects. This calls for practical and theoretical guidance on when the RIAs differ from or coincide with the natural effects. We develop the first empirical test to detect the divergence between the natural effects and their RIAs under the weak assumptions sufficient for identifying the RIAs and illustrate the test using the Moving to Opportunity Study. We also provide new theoretical insights on the relationship between the natural effects and the RIAs both using a covariance formulation and from a structural equation perspective. This analysis also reveals previously undocumented connections between the natural effects, the RIAs, and estimands in instrumental variable analysis and Wilcoxon-Mann-Whitney tests.