Comparer des méthodes

	Tests placebo pour l'inférence causale	Identification causale avec les graphes acycliques dirigés (do-calculus)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	2009
Auteur d'origine	Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)	Judea Pearl
Type	Falsification / robustness test family for causal inference	Causal identification framework
Source fondatrice	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606
Alias	falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Apparentées	5	5
Résumé	Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008).	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.