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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Normalizing the causality between time series.

X San Liang1

  • 1Nanjing University of Information Science and Technology (Nanjing Institute of Meteorology), Nanjing 210044, and China Institute for Advanced Study, Central University of Finance and Economics, Beijing 100081, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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A new formula quantifies causality between time series by normalizing information flow. This method revealed strong one-way causality from International Business Machines Corporation (IBM) to General Electric Company (GE) in their early mainframe market competition.

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Area of Science:

  • Quantitative causality analysis
  • Time series analysis
  • Information flow dynamics

Background:

  • Assessing causality in time series data is crucial for understanding complex systems.
  • Existing methods often lack quantitative rigor or normalization for importance.
  • The evolution of information flow requires a normalized measure to determine significance.

Purpose of the Study:

  • To derive a formula for quantitatively evaluating information flow and causality between time series.
  • To develop a normalization method for assessing the importance of identified causality.
  • To apply the derived method to a real-world financial analysis problem.

Main Methods:

  • Derivation of a concise formula for information flow and causality.
  • Normalization of causality by distinguishing Lyapunov exponent-like stretching rate and noise-to-signal ratio.
  • Verification using autoregressive models.
  • Application to historical financial data.

Main Results:

  • A rigorous formula for quantitative causality assessment was successfully derived and verified.
  • Normalization effectively distinguishes the significance of information flow.
  • An unusually strong one-way causality was identified from International Business Machines Corporation (IBM) to General Electric Company (GE).

Conclusions:

  • The developed method provides a robust framework for quantifying and normalizing causality in time series.
  • The identified causality between IBM and GE offers insights into historical market dynamics.
  • This approach has potential applications in various fields requiring causal inference from time-dependent data.