Constraint- and Score-Based Nonlinear Granger Causality Discovery with Kernels
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Abstract (arXiv)
Kernel-based methods are used in the context of Granger Causality to enable the identification of nonlinear causal relationships between time series variables. In this paper, we show that two state of the art kernel-based Granger Causality (GC) approaches can be theoretically unified under the framework of Kernel Principal Component Regression (KPCR), and introduce a method based on this unification, demonstrating that this approach can improve causal identification. Additionally, we introduce a Gaussian Process score-based model with Smooth Information Criterion penalisation on the marginal likelihood, and demonstrate improved performance over existing state of the art time-series nonlinear causal discovery methods. Furthermore, we propose a contemporaneous causal identification algorithm, fully based on GC, using the proposed score-based GPSIC method, and compare its performance to a state of the art contemporaneous time series causal discovery algorithm.
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References · 6
- 2017Detecting causal associations in large nonlinear time series datasetscited by 240
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
- 2016Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysiscited by 92
- 2020Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series datacited by 12
- 2021Variable Selection Using a Smooth Information Criterion for Distributional Regression Modelscited by 2
- 2016Sparse Estimation of Generalized Linear Models (GLM) via Approximated Information Criteriacited by 1
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