Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Mediación Causal (Efectos Directos e Indirectos Naturales)× | Modelado Lineal Jerárquico (HLM / Modelado Multinivel)× | |
|---|---|---|
| Campo≠ | Inferencia causal | Estadística |
| Familia≠ | Regression model | Hypothesis test |
| Año de origen≠ | 2010 | 1986 |
| Autor original≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| Tipo≠ | Counterfactual causal decomposition | Parametric nested-data regression |
| Fuente seminal≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| Alias≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | HLM, MLM, multilevel modeling, multilevel analysis |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | 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. | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. |
| ScholarGateConjunto de datos ↗ |
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