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Criteria for Causality: Bradford Hill Criteria - II01:28

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Criteria for Causality: Bradford Hill Criteria - I01:30

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Synergy, redundancy and unnormalized Granger causality.

S Stramaglia, L Angelini, J M Cortes

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    Summary
    This summary is machine-generated.

    We analyzed Granger causality to understand information flow in complex networks. Maximizing Granger causality reveals redundant variable groups, while a new synergy index identifies additive influences.

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    Area of Science:

    • Complex systems analysis
    • Information theory
    • Network science

    Background:

    • Understanding information flow between subsystems is crucial for complex network analysis.
    • Synergy and redundancy are key factors influencing information transfer.
    • Existing methods may not fully capture these effects.

    Purpose of the Study:

    • To investigate the impact of synergy and redundancy on inferring information flow using Granger causality.
    • To develop methods that can reveal synergetic and redundant effects in complex networks.

    Main Methods:

    • Analysis using Granger causality on time series data.
    • Exploration of fully conditioned and pairwise Granger causality.
    • Maximization of total Granger causality over variable partitions.
    • Introduction of a novel pairwise synergy index.

    Main Results:

    • Fully conditioned Granger causality is unaffected by synergy.
    • Pairwise analysis fails to detect synergetic effects.
    • Maximizing Granger causality with an unnormalized definition highlights redundant variable multiplets.
    • The new synergy index is zero for additive influences from independent sources.

    Conclusions:

    • Standard Granger causality methods have limitations in capturing synergy and redundancy.
    • Modified approaches, including maximizing Granger causality and a new synergy index, can better reveal complex information flow patterns.
    • These findings advance the analysis of information dynamics in complex systems.