Causality detection: Difference between revisions

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In the context of data analysis, it is more productive to adopt [[Wikipedia:Norbert_Wiener|Wiener]]'s 'quantifiable causality'. <ref>N. Wiener. ''The theory of prediction.'' Modern Mathematics for Engineers, Mc-Graw Hill, New York, 1956, ISBN 0486497461</ref> It states:
In the context of data analysis, it is more productive to adopt [[Wikipedia:Norbert_Wiener|Wiener]]'s 'quantifiable causality'. <ref>N. Wiener. ''The theory of prediction.'' Modern Mathematics for Engineers, Mc-Graw Hill, New York, 1956, ISBN 0486497461</ref> It states:
* if we can predict X better by using the past information from Y than without it, then we call Y causal to X.
* if we can predict X better by using the past information from Y than without it, then we call Y causal to X.
== Traditional approaches ==
Traditional approaches to the determination of causal relationships between various (fluctuating) variables include a wide range of methods.
* If intervention in the system is possible, one may control (modulate) one variable and observe the (delayed) effect on other variables.
* Observe systematic time delays between characteristic events or observe systematic precursors to charactaristic events.
* Predict the system evolution from a (numerical) model. If successful, the model equations may reveal causal relations.
* Quantify parameters related to system evolution (growth rates, damping rates).
* Use techniques such as correlations, conditional averages; these linear analysis techniques by themselves cannot reveal causality, but additioal reasoning (based on physical insight or models) may allow drawing conclusions. 
In the fusion context, see <ref>K.H. Burrell, ''Tests of causality: Experimental evidence that sheared <math>E \times B</math> flow alters turbulence and transport in tokamaks,[[doi:10.1063/1.873728|'' Phys. Plasmas, '''6'''(12):4418, 1999]]</ref>.


== Analysis techniques ==
== Analysis techniques ==


Several techniques have been elaborated to quantify this statement on the basis of measured time series.
Several techniques have been elaborated to quantify Wiener's causality on the basis of measured time series.
<ref>K. Hlaváková-Schindler, M. Palus, M. Vejmelka, and J. Bhattacharya. ''Causality detection based on information-theoretic approaches in time series analysis'', [[doi:10.1016/j.physrep.2006.12.004|Phys. Reports, '''441'''(1):1, 2007]]</ref>
<ref>K. Hlaváková-Schindler, M. Palus, M. Vejmelka, and J. Bhattacharya. ''Causality detection based on information-theoretic approaches in time series analysis'', [[doi:10.1016/j.physrep.2006.12.004|Phys. Reports, '''441'''(1):1, 2007]]</ref>