  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.
