What is it about?
We have compared methods for estimating how much one time series 'influences' another, in a simple system containing two simultaneously observed components. We have investigated performance on a number of simulated datasets, as well as providing tests for their robustness to problems with data.
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Why is it important?
Understanding cause-and-effects in observational time series data is a difficult challenge, involving contributions from different areas of mathematics. There is not an easy one-size-fits all solution to this problem, which requires careful consideration of other additional potential causes or confounding variables. Our work is a step towards this goal, via a greater understanding of which tools (and when) are suitable for quantifying relationships in a bivariate system.
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This page is a summary of: Causality indices for bivariate time series data: A comparative review of performance, Chaos An Interdisciplinary Journal of Nonlinear Science, August 2021, American Institute of Physics,
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