Bandingkan metode

	Pemodelan Persamaan Struktural Bayesian (BSEM)	Analisis Mediasi Kausal (Efek Langsung dan Tidak Langsung Alami)
Bidang	Bayesian	Inferensi Kausal
Keluarga	Bayesian methods	Regression model
Tahun asal	2012	2010
Pencetus	Bengt Muthén & Tihomir Asparouhov	Pearl (2001); general framework by Imai, Keele & Tingley (2010)
Tipe	Bayesian latent variable model	Counterfactual causal decomposition
Sumber perintis	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.
Alias	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
Terkait	6	5
Ringkasan	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.