  Causality defines the relationship between cause and effect. In multivariate
time series field, this notion allows to characterize the links between several
time series considering temporal lags. These phenomena are particularly
important in medicine to analyze the effect of a drug for example, in
manufacturing to detect the causes of an anomaly in a complex system or in
social sciences... Most of the time, studying these complex systems is made
through correlation only. But correlation can lead to spurious relationships.
To circumvent this problem, we present in this paper a novel approach for
discovering causality in time series data that combines a causal discovery
algorithm with an information theoretic-based measure. Hence the proposed
method allows inferring both linear and non-linear relationships and building
the underlying causal graph. We evaluate the performance of our approach on
several simulated data sets, showing promising results.
