Methoden vergelijken

	Causale Identificatie met Gerichte Acyclische Grafen (do-calculus)	Instrumentele Variabelen via Two-Stage Least Squares (IV/2SLS)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2009	2009
Grondlegger	Judea Pearl	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Causal identification framework	Instrumental-variables regression
Oorspronkelijke bron	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassen	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Verwant	5	5
Samenvatting	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.	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).