Methoden vergleichen

	Granger-Kausalitätstest	Transfer Entropy
Fachgebiet	Ökonometrie	Kausale Inferenz
Familie	Regression model	Machine learning
Entstehungsjahr	1969	2000
Urheber	Clive W. J. Granger	Thomas Schreiber
Typ	Time-series predictive causality test	Non-parametric information-theoretic measure
Wegweisende Quelle	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.
Aliasnamen	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	Schreiber Information Transfer, Directed Information Flow, Conditional Mutual Information (directed), Transfer Entropisi
Verwandt	5	3
Zusammenfassung	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.	Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics.