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Causality detection: Difference between revisions
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Thus, causality typically involves at least a temporal ''delay'' between the two events. | Thus, causality typically involves at least a temporal ''delay'' between the two events. | ||
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. | ||
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Traditional approaches to the determination of causal relationships between various (fluctuating) variables include a wide range of methods. | 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. | * 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 | * Observe systematic time delays between characteristic events or observe systematic precursors to characteristic events. | ||
* Predict the system evolution from a (numerical) model. If successful, the model equations may reveal causal relations. | * 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). | * 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 | * Use techniques such as correlations, conditional averages; these linear analysis techniques by themselves cannot reveal causality, but additional 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| | 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 == | ||