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This study introduces new methods to measure information transfer strength along indirect causal paths in complex systems. It quantifies intermediate process contributions to interaction mechanisms, aiding analysis when models are unavailable.

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Area of Science:

  • Complex Systems Analysis
  • Information Theory
  • Causal Inference

Background:

  • Information transfer measures analyze complex system interactions (e.g., Earth, brain) using time series data.
  • Existing causal definitions focus on predictive information, excluding common drivers and indirect influences.

Purpose of the Study:

  • Develop measures for the strength of information transfer along indirect causal paths.
  • Quantify the contribution of intermediate processes to interaction mechanisms.
  • Determine pathways of causal information transfer.

Main Methods:

  • Reconstruct multivariate causal networks.
  • Develop novel information-theoretic measures for indirect causal paths.
  • Analyze interaction mechanisms beyond predictive decomposition.

Main Results:

  • Proposed framework quantifies indirect information transfer strength.
  • Novel measures assess intermediate process contributions to causal pathways.
  • Mathematical framework provides information-theoretic interpretation and links to dynamics.

Conclusions:

  • The framework offers a method to measure indirect information transfer and interaction mechanisms.
  • This approach aids in abstracting dynamics when experiments or models are lacking.
  • Illustrated on a climatological example for atmospheric flow analysis.