Compara mètodes

	Ajustament per la porta d'entrada (Criteri de la porta d'entrada)	La identificació causal amb grafs acíclics dirigits (do-càlcul)	Variables instrumentals mitjançant mínims quadrats en dues etapes (IV/2SLS)
Camp	Inferència causal	Inferència causal	Inferència causal
Família	Regression model	Regression model	Regression model
Any d'origen	1995	2009	2009
Autor original	Judea Pearl	Judea Pearl	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Tipus	Causal identification (graphical adjustment)	Causal identification framework	Instrumental-variables regression
Font seminal	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
Àlies	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
Relacionats	4	5	5
Resum	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).