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Parametric and Non-parametric Criteria for Causal Inference from Time-Series

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Directed Information Measures in Neuroscience

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Granger causality constitutes a criterion for causal inference from time series that has been largely applied to study causal interactions in the brain from electrophysiological recordings. This criterion underlies the classical parametric implementation in terms of linear autoregressive processes as well as Transfer entropy, i.e. a non-parametric implementation in the framework of information theory. In the spectral domain, partial directed coherence and the Geweke formulation are related to Granger causality but rely on alternative criteria for causal inference which are inherently based on the parametric formulation in terms of autoregressive processes. Here we clearly differentiate between criteria for causal inference and measures used to test them. We compare the different criteria for causal inference from timeseries and we further introduce new criteria that complete a unified picture of how the different approaches are related. Furthermore, we compare the different measures that implement these criteria in the information theory framework.

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Chicharro, D. (2014). Parametric and Non-parametric Criteria for Causal Inference from Time-Series. In: Wibral, M., Vicente, R., Lizier, J. (eds) Directed Information Measures in Neuroscience. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54474-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-54474-3_8

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