مقایسهٔ روش‌ها

	تنظیم پیش‌رو (معیار پیش‌رو)	شناسایی علّی با استفاده از گراف‌های جهت‌دار بدون دور (حساب do)	طرح گسستگی رگرسیون (RDD)
حوزه	استنتاج علّی	استنتاج علّی	استنتاج علّی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1995	2009	2008
پدیدآور	Judea Pearl	Judea Pearl	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
نوع	Causal identification (graphical adjustment)	Causal identification framework	Quasi-experimental causal design
منبع بنیادین	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.	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.
نام‌های دیگر	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
مرتبط	4	5	5
خلاصه	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.	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.