เปรียบเทียบวิธี

	การปรับแบบประตูหน้า (เกณฑ์ประตูหน้า)	ขั้นตอนวิธีค้นหาความเป็นเหตุเป็นผล (PC, FCI, LiNGAM)	การออกแบบการถดถอยแบบไม่ต่อเนื่อง (Regression Discontinuity Design - RDD)	ตัวแปรเครื่องมือผ่านวิธีกำลังสองน้อยที่สุดสองขั้นตอน (IV/2SLS)
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model	Regression model	Regression model
ปีกำเนิด	1995	2000	2008	2009
ผู้ริเริ่ม	Judea Pearl	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
ประเภท	Causal identification (graphical adjustment)	Causal structure learning	Quasi-experimental causal design	Instrumental-variables regression
แหล่งต้นตำรับ	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	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
ชื่อเรียกอื่น	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	RDD, regression discontinuity design, sharp RDD, fuzzy RDD	instrumental variables, IV estimation, 2SLS, instrumental variable regression
ที่เกี่ยวข้อง	4	5	5	5
สรุป	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.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.	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).