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Fast and effective pseudo transfer entropy for bivariate data-driven causal inference.

Riccardo Silini1, Cristina Masoller2

  • 1Departament de Física, Universitat Politècnica de Catalunya, Rambla St. Nebridi 22, 08222, Terrassa, Spain. riccardo.silini@outlook.com.

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Summary
This summary is machine-generated.

We introduce pseudo transfer entropy (pTE), a computationally efficient method for causal inference from time series data. pTE offers similar accuracy to Granger causality but significantly reduces computational cost, especially for short time series.

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Area of Science:

  • Complex systems analysis
  • Computational neuroscience
  • Statistical inference

Background:

  • Distinguishing correlation from causality is a fundamental challenge in time series analysis.
  • Existing causal inference methods often have limitations in data requirements, computational cost, or parameter complexity.
  • The need for efficient and robust causality detection is critical across various scientific disciplines.

Purpose of the Study:

  • To develop a computationally efficient measure for causality testing in time series analysis.
  • To address the limitations of existing methods, particularly for short or noisy datasets.
  • To provide a valuable tool for inferring causality networks from large-scale time series data.

Main Methods:

  • Derivation of pseudo transfer entropy (pTE) from transfer entropy (TE) using a Gaussian approximation.
  • Application and evaluation of pTE on both simulated and real-world time series data.
  • Comparison of pTE performance against Granger causality (GC) and significance testing methods like iterative amplitude adjusted Fourier transform (IAAFT) surrogates.

Main Results:

  • pTE demonstrates high similarity in results to Granger causality (GC).
  • pTE combined with time-shifted (T-S) surrogates significantly reduces computational cost for short time series compared to GC and IAAFT.
  • The proposed method exhibits robustness against observational noise.

Conclusions:

  • Pseudo transfer entropy (pTE) offers a computationally efficient and accurate approach to causal inference.
  • The method is particularly advantageous for analyzing large numbers of short time series.
  • pTE is a valuable tool for inferring causality networks in complex systems.