Causality detection based on information-theoretic approaches in time series analysis

By Katerina Hlaváčková-Schindler, Milan Palus, Martin Vejmelka, Joydeep Bhattacharya. 2007

819 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/906b4b987cebdb945cae1f6aec8e14873dc0cf79

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In Artificial Intelligence

In Control and Systems Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Complex network approaches to nonlinear time series analysis (2019, background): “We do not expand the corresponding discussion here, but refer the interested readers to several review papers on this topic [23,38,39].”
Equivalence of Granger Causalityand Transfer Entropy: A Generalization. (2011, neutral): “More on the information-theoretic methods for causality detection can be found in our review paper [13].”
Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data (2016, background): “Hlaváčková-Schindler et al. (2007) explore the information theoretic concepts for detecting causality in time series.”
Causal network reconstruction from time series: From theoretical assumptions to practical estimation. (2018, background): “…nonlinear dynamics studies the properties of nonlinear and chaotic dynamical processes which has led to a plethora of nonlinear time series analysis methods (Kantz and Schreiber, 2003), often from an information-theoretic perspective (Hlavácková-Schindler et al., 2007) including transfer entropy.”
p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, background): “We further propose a a Granger causality score GC score to generate initial causal pathways, which is deﬁned as: where GC score is a χ 2 -test score [12] for the predictive causality, with higher score indicating more probable “Granger” causes from M pollutants at sensor s n to the target pollutant…”
Detecting Causality in Complex Ecosystems (2012, background): “Conditional Mutual Information There is a large body of work on detecting causality in time series using the concept of conditional mutual information (CMI) and the equivalent concept of transfer entropy (55, 61-65, 67, 68).”
Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings (2014, methodology): “Nonparametric density estimationmethods previously developed formutual information [8,36] are likely to offer a route toward the reliable estimation of causation entropies in large-scale dynamical systems.”
Transfer entropy—a model-free measure of effective connectivity for the neurosciences (2010, methodology): “As with other informationtheoretic functionals, any estimate shows biases and statistical errors which depend on the method used and the characteristics of the data (Hlavackova-Schindler et al. 2007; Kraskov et al. 2004).”
Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs (2010, methodology): “…information estimators have been used in feature selection (Peng and Ding, 2005), clustering (Aghagolzadeh et al., 2007), causality detection (Hlav´ackova-Schindler et al., 2007), optimal experimental design (Lewi et al., 2007; P´oczos and L˝orincz, 2009), fMRI data processing (Chai et al.,…”
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, background): “…(Kantz and Schreiber, 2003; Datseris and Parlitz, 2022), synchronization theory (Arnhold et al., 1999; Pikovsky et al., 2001; Stankovski et al., 2012; Rosenblum and Pikovsky, 2023), statistical physics (Tabar, 2019), and information theory (Hlavá č ková-Schindler et al., 2007), among others.”
Brain causality alterations in major depressive disorder treatment (2026, unknown): “The information-theoretic generalization of the Granger causality concept (16) provides a solid mathematical basis for several reliable causality inference methods (17, 18), including the phase dynamics adaptation used in this study (19).”