Neural Granger Causality

By Alex Tank, Ian Covert, N. Foti, Ali Shojaie, Emily B. Fox. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

403 citations recorded by the source corpus.

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero—in particular, through the use of convex group-lasso penalties—we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ce18e93b5654065c7dbc56aaa93e39fc355ed7c4

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Machine learning and deep learning—A review for ecologists (2022, methodology): “Another interesting approach is to combine statistical models with ML and DL algorithms (Joseph, 2020 ; Masahiro & Rillig, 2017 ; Tank et al., 2021 ).”