Bandingkan kaedah

	Penyesuaian Pintu Depan (Kriteria Pintu Depan)	Algoritma Penemuan Kausal (PC, FCI, LiNGAM)
Bidang	Inferens Kausal	Inferens Kausal
Keluarga	Regression model	Regression model
Tahun asal	1995	2000
Pengasas	Judea Pearl	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
Jenis	Causal identification (graphical adjustment)	Causal structure learning
Sumber perintis	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
Alias	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning
Berkaitan	4	5
Ringkasan	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.