On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems

Sinha, Subhrajit; Vaidya, Umesh

doi:10.1007/s00332-020-09620-1

Cited by 16 publications

(8 citation statements)

References 31 publications

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“…In our work, we adopt Liang-Kleeman’s formalism of information transfer to measure the flow of information in a network. This formalism has been used to understand causal inference using time series data in large-scale networks 38 and for identifying sources of instability in network power systems 39 .…”

Section: Introductionmentioning

confidence: 99%

On quantification and maximization of information transfer in network dynamical systems

Singh

Pasumarthy

Vaidya

et al. 2023

Sci Rep

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Information flow among nodes in a complex network describes the overall cause-effect relationships among the nodes and provides a better understanding of the contributions of these nodes individually or collectively towards the underlying network dynamics. Variations in network topologies result in varying information flows among nodes. We integrate theories from information science with control network theory into a framework that enables us to quantify and control the information flows among the nodes in a complex network. The framework explicates the relationships between the network topology and the functional patterns, such as the information transfers in biological networks, information rerouting in sensor nodes, and influence patterns in social networks. We show that by designing or re-configuring the network topology, we can optimize the information transfer function between two chosen nodes. As a proof of concept, we apply our proposed methods in the context of brain networks, where we reconfigure neural circuits to optimize excitation levels among the excitatory neurons.

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Section: Introductionmentioning

confidence: 99%

On quantification and maximization of information transfer in network dynamical systems

Singh

Pasumarthy

Vaidya

et al. 2023

Sci Rep

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“…In the realm of dynamical systems, a new definition and measure of causal characterization, called information transfer, has been proposed recently [28,29,30], where the authors show that the existing definitions of causality, namely, Granger causality, directed information, and transfer entropy fail to capture the correct causal structure in a dynamical system. Additionally, some recent studies [31,32] provided a data-driven approach to infer the causal structure of a dynamical system from time-series data.…”

Section: Introductionmentioning

confidence: 99%

Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic

Sinha¹,

Chakraborty²

2021

Preprint

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Since the increasing outspread of COVID-19 in the U.S., with the highest number of confirmed cases and deaths in the world as of September 2020, most states in the country have enforced travel restrictions resulting in sharp reductions in mobility. However, the overall impact and long-term implications of this crisis to travel and mobility remain uncertain. To this end, this study develops an analytical framework that determines and analyzes the most dominant factors impacting human mobility and travel in the U.S. during this pandemic. In particular, the study uses Granger causality to determine the important predictors influencing daily vehicle miles traveled and utilize linear regularization algorithms, including Ridge and LASSO techniques, to model and predict mobility. State-level time-series data were obtained from various open-access sources for the period starting from March 1, 2020 through June 13, 2020 and the entire data set was divided into two parts for training and testing purposes. The variables selected by Granger causality were used to train the three different reduced order models by ordinary least square regression, Ridge regression, and LASSO regression algorithms. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that the factors including the number of new COVID cases, social distancing index, population staying at home, percent of out of county trips, trips to different destinations, socioeconomic status, percent of people working from home, and statewide closure, among others, were the most important factors influencing daily VMT. Also, among all the modeling techniques, Ridge regression provides the most superior performance with the least error, while LASSO regression also performed better than the ordinary least square model.

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“…The idea of freezing alleviates the problem associated with other information-based causality measures and captures the direct causal links as shown by Sinha and Vaidya (2016). However, unlike the previous research by Sinha and Vaidya (2020), in the proposed definition of IT, the 'change in uncertainty' is computed using KL divergence. In summary, the essential novelty of the proposed definition of IT is that it quantifies the influence of a subset of states on other states and a combination of states.…”

Section: Introductionmentioning

confidence: 99%

Information-Theoretic Approach for Model Reduction Over Finite Time Horizon

Tulpule¹,

Vaidya²

2021

Preprint

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This paper presents an information-theoretic approach for model reduction for finite time simulation. Although system models are typically used for simulation over a finite time, most of the metrics (and pseudo-metrics) used for model accuracy assessment consider asymptotic behavior e.g., Hankel singular values and Kullback-Leibler(KL) rate metric. These metrics could further be used for model order reduction. Hence, in this paper, we propose a generalization of KL divergence-based metric called n-step KL rate metric, which could be used to compare models over a finite time horizon. We then demonstrate that the asymptotic metrics for comparing dynamical systems may not accurately assess the model prediction uncertainties over a finite time horizon. Motivated by this finite time analysis, we propose a new pragmatic approach to compute the influence of a subset of states on a combination of states called information transfer (IT). Model reduction typically involves the removal or truncation of states. IT combines the concepts from the n-step KL rate metric and model reduction. Finally, we demonstrate the application of information transfer for model reduction. Although the analysis and definitions presented in this paper assume linear systems, they can be extended for nonlinear systems.

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On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems

Cited by 16 publications

References 31 publications

On quantification and maximization of information transfer in network dynamical systems

On quantification and maximization of information transfer in network dynamical systems

Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic

Information-Theoretic Approach for Model Reduction Over Finite Time Horizon

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