Comparer des méthodes

	Ajustement par la porte de devant (Critère de la porte de devant)	Identification causale avec les graphes acycliques dirigés (do-calculus)	Variables instrumentales par moindres carrés en deux étapes (VI/2SLS)
Domaine	Inférence causale	Inférence causale	Inférence causale
Famille	Regression model	Regression model	Regression model
Année d'origine	1995	2009	2009
Auteur d'origine	Judea Pearl	Judea Pearl	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Causal identification (graphical adjustment)	Causal identification framework	Instrumental-variables regression
Source fondatrice	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.	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
Alias	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Apparentées	4	5	5
Résumé	Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.	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).