Non-parametric causal inference for bivariate time series

James M. McCracken; Robert S. Weigel

doi:10.1103/physreve.93.022207

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arXiv · 1506.06823

Non-parametric causal inference for bivariate time series

James M. McCracken·Robert S. Weigel

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Abstract

We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

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James M. McCracken, Robert S. Weigel. 2016-01-27. Non-parametric causal inference for bivariate time series. https://doi.org/10.1103/physreve.93.022207

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