  Given two time series, can one tell, in a rigorous and quantitative way, the
cause and effect between them? Based on a recently rigorized physical notion
namely information flow, we arrive at a concise formula and give this
challenging question, which is of wide concern in different disciplines, a
positive answer. Here causality is measured by the time rate of change of
information flowing from one series, say, X2, to another, X1. The measure is
asymmetric between the two parties and, particularly, if the process underlying
X1 does not depend on X2, then the resulting causality from X2 to X1 vanishes.
The formula is tight in form, involving only the commonly used statistics,
sample covariances. It has been validated with touchstone series purportedly
generated with one-way causality. It has also been applied to the investigation
of real world problems; an example presented here is the cause-effect relation
between two climate modes, El Ni\~no and Indian Ocean Dipole, which have been
linked to the hazards in far flung regions of the globe, with important results
that would otherwise be difficult, if not impossible, to obtain.
