Compara mètodes

	Ajustament per la porta d'entrada (Criteri de la porta d'entrada)	Algorismes de descobriment causal (PC, FCI, LiNGAM)	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	2000	2009
Autor original	Judea Pearl	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Tipus	Causal identification (graphical adjustment)	Causal structure learning	Instrumental-variables regression
Font seminal	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	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)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	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.	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.	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).