Methods for Reconstructing Causal Networks from Observed Time-Series: Granger-Causality, Transfer Entropy, and Convergent Cross-Mapping

Abstract

A major question that arises in many areas of Cognitive Science is the need to distinguish true causal connections between variables from mere correlations. The most common way of addressing this distinction is the design of well-controlled experiments. However, in many situations, it is extremely difficult --or even outright impossible-- to perform such experiments. Researchers are then forced to rely on correlational data in order to make causal inferences. This situation is especially common when one needs to analyze longitudinal data corresponding to historical time-series, symbolic sequences, or developmental data. These inferences are problematic. From the correlations alone it is difficult to determine the direction of the causal arrow linking two variables. Worse even, the lack of controls of observational data entail that correlations found between two variables need not reflect any causal connection between them, as some third variable could be the actually driver for any two measured variables.


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