# A simple test for causality in complex systems

@article{Haaga2020AST, title={A simple test for causality in complex systems}, author={Kristian Agas{\o}ster Haaga and David Diego and Jo Brendryen and Bjarte Hannisdal}, journal={arXiv: Applications}, year={2020} }

We provide a new solution to the long-standing problem of inferring causality from observations without modeling the unknown mechanisms. We show that the evolution of any dynamical system is related to a predictive asymmetry that quantifies causal connections from limited observations. A built-in significance criterion obviates surrogate testing and drastically improves computational efficiency. We validate our test on numerous synthetic systems exhibiting behavior commonly occurring in nature… Expand

#### One Citation

Causality in Reversed Time Series: Reversed or Conserved?

- Computer Science, Medicine
- Entropy
- 2021

Simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. Expand

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