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	Bayesiansk strukturekvivalentmodellering (BSEM)	Kausal medieringsanalys (naturliga direkta och indirekta effekter)	Regressionsdiskontinuitetsdesign (RDD)
Ämnesområde	Bayesiansk statistik	Kausal inferens	Kausal inferens
Familj	Bayesian methods	Regression model	Regression model
Ursprungsår	2012	2010	2008
Upphovsperson	Bengt Muthén & Tihomir Asparouhov	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Typ	Bayesian latent variable model	Counterfactual causal decomposition	Quasi-experimental causal design
Ursprungskälla	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.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
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	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Närliggande	6	5	5
Sammanfattning	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.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.