EAN: 9783031007811

SYNTHESIS LECTURES ON DATA MINING AND KNOWLEDGE...

Many scientific disciplines rely on observational data of systems for which it is difficult (or
impossible) to implement controlled experiments. Data analysis techniques are required for
identifying causal information and relationships directly from such observational data. This
need has led to the development of many different time series causality approaches and tools
including transfer entropy  convergent cross-mapping (CCM)  and Granger causality statistics. A
practicing analyst can explore the literature to find many proposals for identifying drivers
and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a
framework for exploring potential causal structures in time series data sets and is
characterized by a myopic goal to determine which data series from a given set of series might
be seen as the primary driver. In this work  ECA is used on several synthetic and empirical
data sets  and it is found that all of the tested time series causality tools agree with each
other (and intuitive notions of causality) for many simple systems but can provide conflicting
causal inferences for more complicated systems. It is proposed that such disagreements between
different time series causality tools during ECA might provide deeper insight into the data
than could be found otherwise.

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