Causal discovery for time series with constraint-based model and PMIME measure
This addresses the need for more accurate causality analysis in fields like medicine and manufacturing, though it appears incremental as it builds on existing causal discovery and information theory methods.
The paper tackles the problem of distinguishing true causal relationships from spurious ones in multivariate time series data, presenting a novel approach that combines a causal discovery algorithm with an information-theoretic measure, which shows promising results on simulated datasets.
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.