Comparar métodos

	Pruebas de placebo para inferencia causal	Algoritmos de descubrimiento causal (PC, FCI, LiNGAM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010	2000
Autor original	Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
Tipo	Falsification / robustness test family for causal inference	Causal structure learning
Fuente seminal	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.	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402
Alias	falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama	PC algorithm, FCI algorithm, LiNGAM, causal structure learning
Relacionados	5	5
Resumen	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).	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.