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Prilagođavanje prednjim vratima (Kriterijum prednjih vrata)×Algoritmi za otkrivanje uzročnosti (PC, FCI, LiNGAM)×
OblastKauzalno zaključivanjeKauzalno zaključivanje
PorodicaRegression modelRegression model
Godina nastanka19952000
TvoracJudea PearlSpirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
TipCausal identification (graphical adjustment)Causal structure learning
Temeljni izvorPearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688. DOI ↗Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402
Drugi nazivifrontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)PC algorithm, FCI algorithm, LiNGAM, causal structure learning
Srodne45
SažetakFrontdoor 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.
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ScholarGateUporedite metode: Frontdoor Adjustment · Causal Discovery Algorithms. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare