A note on causation versus correlation

Abstract

Recently, it has been shown that the causality and information flow between two time series can be inferred in a rigorous and quantitative sense, and, besides, the resulting causality can be normalized. A corollary that follows is, in the linear limit, causation implies correlation, while correlation does not imply causation. Now suppose there is an event $A$ taking a harmonic form (sine/cosine), and it generates through some process another event $B$ so that $B$ always lags $A$ by a phase of $\pi /2$. Here the causality is obviously seen, while by computation the correlation is, however, zero. This seemingly contradiction is rooted in the fact that a harmonic system always leaves a single point on the Poincaré section; it does not add information. That is to say, though the absolute information flow from $A$ to $B$ is zero, i.e., $T_{A\to B}=0$, the total information increase of $B$ is also zero, so the normalized $T_{A\to B}$, denoted as ${\tau }_{A\to B}$, takes the form of $\frac{0}{0}$. By slightly perturbating the system with some noise, solving a stochastic differential equation, and letting the perturbation go to zero, it can be shown that ${\tau }_{A\to B}$ approaches 100\%, just as one would have expected.

5 pages

References in corpus (1)

• Normalizing the causality between time series