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	Analiza procesu warunkowego (mediacja moderowana)	Bayesowskie modelowanie równań strukturalnych (BSEM)	Analiza przyczynowego pośrednictwa (naturalny efekt bezpośredni i pośredni)
Dziedzina	Wnioskowanie przyczynowe	Statystyka bayesowska	Wnioskowanie przyczynowe
Rodzina	Regression model	Bayesian methods	Regression model
Rok powstania	2018	2012	2010
Twórca	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Bengt Muthén & Tihomir Asparouhov	Pearl (2001); general framework by Imai, Keele & Tingley (2010)
Typ	Regression-based conditional process model	Bayesian latent variable model	Counterfactual causal decomposition
Źródło pierwotne	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	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.
Inne nazwy	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
Pokrewne	5	6	5
Podsumowanie	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	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.