Comparer des méthodes

	Identification causale avec les graphes acycliques dirigés (do-calculus)	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Inférence causale	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	2009	1990s (modern applications)
Auteur d'origine	Judea Pearl	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal identification framework	Method
Source fondatrice	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: Princeton University Press.
Alias	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	IV, two-stage least squares, TSLS, causal estimation
Apparentées	5	3
Résumé	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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.