title: Nonparametric causal inference for bivariate time series.
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.
authors.affiliation: Department of Physics and Astronomy George Mason University and 4400 University Drive MS 3F3, Fairfax, Virginia 22030-4444, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: McCracken
authors.affiliation: Department of Physics and Astronomy George Mason University and 4400 University Drive MS 3F3, Fairfax, Virginia 22030-4444, USA.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Weigel