Comparer des méthodes

	Identification causale avec les graphes acycliques dirigés (do-calculus)	La régression par discontinuité (RDD)	Variables instrumentales par moindres carrés en deux étapes (VI/2SLS)
Domaine	Inférence causale	Inférence causale	Inférence causale
Famille	Regression model	Regression model	Regression model
Année d'origine	2009	2008	2009
Auteur d'origine	Judea Pearl	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Causal identification framework	Quasi-experimental causal design	Instrumental-variables regression
Source fondatrice	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.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	RDD, regression discontinuity design, sharp RDD, fuzzy RDD	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Apparentées	5	5	5
Résumé	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.	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).