Neural Granger Causality

Abstract

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.

IEEE TPAMI accepted version

References in corpus (21)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio
• Regularized estimation in sparse high-dimensional time series models
• Bayesian Compression for Deep Learning
• Learning with Structured Sparsity
• A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
• A Clockwork RNN
• Proximal Methods for Hierarchical Sparse Coding
• Kernel Granger causality and the analysis of dynamical networks
• Learning the Number of Neurons in Deep Networks
• Extensive Chaos in the Lorenz-96 Model
• Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
• On directed information theory and Granger causality graphs
• Joint modeling of multiple time series via the beta process with application to motion capture segmentation
• Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
• Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification
• Network Granger Causality with Inherent Grouping Structure
• An interpretable LSTM neural network for autoregressive exogenous model
• Inference of High-dimensional Autoregressive Generalized Linear Models
• Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
• Granger Causality Networks for Categorical Time Series

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