Methoden vergleichen

	Algorithmen zur kausalen Entdeckung (PC, FCI, LiNGAM)	Regression Discontinuity Design (RDD)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2000	2008
Urheber	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Typ	Causal structure learning	Quasi-experimental causal design
Wegweisende Quelle	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Aliasnamen	PC algorithm, FCI algorithm, LiNGAM, causal structure learning	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Verwandt	5	5
Zusammenfassung	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.	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.