方法对比
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| 因果中介分析（自然直接效应和自然间接效应）× | 有条件过程分析（有调节的中介）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 2018 |
| 提出者≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation) |
| 类型≠ | Counterfactual causal decomposition | Regression-based conditional process model |
| 开创性文献≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654 |
| 别名 | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals. |
| ScholarGate数据集 ↗ |
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