Methoden vergleichen

	Kausale Identifikation mit gerichteten azyklischen Graphen (do-calculus)	Markov-Kette Monte Carlo (MCMC)	Strukturelle Gleichungsmodellierung
Fachgebiet	Kausale Inferenz	Bayes-Statistik	Forschungsstatistik
Familie	Regression model	Bayesian methods	Process / pipeline
Entstehungsjahr	2009	—	1921
Urheber	Judea Pearl	—	Sewall Wright
Typ	Causal identification framework	Posterior sampling algorithm	Method
Wegweisende Quelle	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Aliasnamen	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	SEM, path analysis, latent variable modeling, causal modeling
Verwandt	5	3	3
Zusammenfassung	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.