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• W3152534294 abstract "Abstract Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by $$82%$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>82</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series." @default.
• W3152534294 created "2021-04-26" @default.
• W3152534294 creator A5025650967 @default.
• W3152534294 creator A5084826813 @default.
• W3152534294 date "2021-04-19" @default.
• W3152534294 modified "2023-10-02" @default.
• W3152534294 title "Fast and effective pseudo transfer entropy for bivariate data-driven causal inference" @default.
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• W3152534294 doi "https://doi.org/10.1038/s41598-021-87818-3" @default.
• W3152534294 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8055902" @default.
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• W3152534294 hasPublicationYear "2021" @default.
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