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| Anàlisi de mediació causal (efectes directes i indirectes naturals)× | Regressió per Mínims Quadrats Ordinàris (MQO)× | |
|---|---|---|
| Camp≠ | Inferència causal | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 2010 | 2019 |
| Autor original≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | Wooldridge (textbook treatment); classical least squares |
| Tipus≠ | Counterfactual causal decomposition | Linear regression |
| Font seminal≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Àlies≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionats | 5 | 5 |
| Resum≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateConjunt de dades ↗ |
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