Sparse Causal Discovery in Multivariate Time Series

Details Sparse Causal Discovery in Multivariate Time Series Sparse Causal Discovery in Multivariate Time Series

2009-01-15

arxiv.org/abs/0901.2234v1

JMLR Workshop and Conference Proceedings 6: Causality: Objectives and Assessment (NIPS 2008), 97 - 106

Statistics

Methodology

to appear in Journal of Machine Learning Research, Proceedings of the NIPS'08 workshop on Causality

Scientific paper

Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an additional sparsity-promoting regularization. Along this line we here propose that sparsity should be enforced for the subgroups of coefficients that belong to each pair of time series, as the absence of a causal relation requires the coefficients for all time-lags to become jointly zero. Such behavior can be achieved by means of l1-l2-norm regularized regression, for which an efficient active set solver has been proposed recently. Our method is shown to outperform standard methods in recovering simulated causality graphs. The results are on par with a second novel approach which uses multiple statistical testing.

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