Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse causale de médiation (effets directs et indirects naturels)× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Inférence causale | Statistiques de recherche |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2010 | 1958 |
| Auteur d'origine≠ | Pearl (2001); general framework by Imai, Keele & Tingley (2010) | David Roxbee Cox |
| Type≠ | Counterfactual causal decomposition | Method |
| Source fondatrice≠ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alias≠ | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation | logit model, binomial logistic regression, LR |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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