Causality detection: Difference between revisions
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The determination of a causal interaction between fluctuating variables in a complex system, such as a fusion-grade plasma, is not straightforward.
Definition of causality
To start with, it is not even easy to define what causality means exactly. [1][2] In philosophy, causality typically refers to a relation between two events X and Y such that:
- if Y occurs, then X will occur; or
- if X occurs, then Y must have occured.
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 Wiener's 'quantifiable causality'. [3] 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.
Analysis techniques
Several techniques have been elaborated to quantify this statement on the basis of measured time series. [4]
Recently, a specific technique taken from Information Theory (the 'Transfer Entropy')[5] was applied succesfully to fluctuation data from fusion devices. [6]
References
- ↑ Wikipedia:Causality
- ↑ Wikipedia:Causality_(physics)
- ↑ N. Wiener. The theory of prediction. Modern Mathematics for Engineers, Mc-Graw Hill, New York, 1956
- ↑ K. Hlaváková-Schindler, M. Palus, M. Vejmelka, and J. Bhattacharya. Causality detection based on information-theoretic approaches in time series analysis, Phys. Reports, 441(1):1, 2007
- ↑ T. Schreiber, Measuring information transfer, Phys. Rev. Lett., 85(2):461, 2000
- ↑ B.Ph. van Milligen, G. Birkenmeier, M. Ramisch, T. Estrada, C. Hidalgo, and A. Alonso, Causality detection and turbulence in fusion plasmas, Nucl. Fusion 54 (2014), 023011