Sammenlign metoder

	Frontdoor Adjustment (Frontdoor Criterion)	Algoritmer til kausal opdagelse (PC, FCI, LiNGAM)	Kausal identifikation med rettede acykliske grafer (do-calculus)	Instrumentalvariable via totrins mindste kvadraters metode (IV/2SLS)
Fagområde	Kausal inferens	Kausal inferens	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model	Regression model	Regression model
Oprindelsesår	1995	2000	2009	2009
Ophavsperson	Judea Pearl	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Causal identification (graphical adjustment)	Causal structure learning	Causal identification framework	Instrumental-variables regression
Oprindelig kilde	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	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
Aliasser	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Relaterede	4	5	5	5
Resumé	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.	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).