Compară metode

	Modelarea Ecuațiilor Structurale Bayesiană (BSEM)	Analiza de Mediere Cauzală (Efecte Directe și Indirecte Naturale)
Domeniu	Bayesian	Inferență cauzală
Familie	Bayesian methods	Regression model
Anul apariției	2012	2010
Autorul original	Bengt Muthén & Tihomir Asparouhov	Pearl (2001); general framework by Imai, Keele & Tingley (2010)
Tip	Bayesian latent variable model	Counterfactual causal decomposition
Sursa seminală	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.
Denumiri alternative	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
Înrudite	6	5
Rezumat	Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables.	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.