Võrdle meetodeid

	Kausalidentifitseerimine suunatud atsükliliste graafide abil (do-arvutus)	Struktuurvõrrandite modelleerimine
Valdkond	Põhjuslik järeldamine	Uurimisstatistika
Perekond	Regression model	Process / pipeline
Tekkeaasta	2009	1921
Looja	Judea Pearl	Sewall Wright
Tüüp	Causal identification framework	Method
Algallikas	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	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.
Rööpnimetused	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	SEM, path analysis, latent variable modeling, causal modeling
Seotud	5	3
Kokkuvõte	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.	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.