Порівняння методів

	Коригування "передніх дверей" (критерій "передніх дверей")	Алгоритми причинно-наслідкового виявлення (PC, FCI, LiNGAM)	DAG Causal Identification	Регресійний розривний дизайн (RDD)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model	Regression model	Regression model
Рік появи	1995	2000	2009	2008
Автор методу	Judea Pearl	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Judea Pearl	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Тип	Causal identification (graphical adjustment)	Causal structure learning	Causal identification framework	Quasi-experimental causal design
Основоположне джерело	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	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Інші назви	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)	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Пов'язані	4	5	5	5
Підсумок	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.	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.