Causality detection

From FusionWiki
Jump to: navigation, search

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.

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 characteristic 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 additional reasoning (based on physical insight or models) may allow drawing conclusions.

In the fusion context, see [4].

Analysis techniques

Several techniques have been elaborated to quantify Wiener's causality on the basis of measured time series. [5]

Recently, a specific technique taken from Information Theory (the Transfer Entropy)[6] was applied succesfully to fluctuation data from fusion devices. [7]


  1. Causality
  2. Causality_(physics)
  3. N. Wiener. The theory of prediction. Modern Mathematics for Engineers, Mc-Graw Hill, New York, 1956, ISBN 0486497461
  4. K.H. Burrell, Tests of causality: Experimental evidence that sheared $ E \times B $ flow alters turbulence and transport in tokamaks, Phys. Plasmas, 6(12):4418, 1999
  5. 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
  6. T. Schreiber, Measuring information transfer, Phys. Rev. Lett., 85(2):461, 2000
  7. 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