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| Analisi di mediazione causale (effetti diretti e indiretti naturali)× | Analisi di Moderazione (Interazione)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2010 | 2018 |
| Ideatore≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Aiken & West (1991); Hayes (PROCESS, 2018) |
| Tipo≠ | Counterfactual causal decomposition | Linear regression with interaction term |
| Fonte seminale≠ | 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 (2nd ed.). Guilford Press. ISBN: 978-1462534654 |
| Alias≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | interaction analysis, moderated regression, simple moderation, Düzenleyici Değişken Analizi (Moderation / İnteraksiyon) |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | 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). |
| ScholarGateInsieme di dati ↗ |
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