方法对比
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| 调节（交互）分析× | 因果中介分析（自然直接效应和自然间接效应）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2018 | 2010 |
| 提出者≠ | Aiken & West (1991); Hayes (PROCESS, 2018) | Pearl (2001); general framework by Imai, Keele & Tingley (2010) |
| 类型≠ | Linear regression with interaction term | Counterfactual causal decomposition |
| 开创性文献≠ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis (2nd ed.). Guilford Press. ISBN: 978-1462534654 | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ |
| 别名≠ | interaction analysis, moderated regression, simple moderation, Düzenleyici Değişken Analizi (Moderation / İnteraksiyon) | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation |
| 相关 | 5 | 5 |
| 摘要≠ | Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018). | Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation. |
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