Sparse Causal Discovery in Multivariate Time Series

Causality: Objectives and Assessment (NIPS 2008 Workshop)

Stefan Haufe [e-mail] ; Machine Learning Group ; TU Berlin Franklinstr. 28/29 ; 10587 Berlin ; Germany ; Intelligent Data Analysis Group ; Fraunhofer FIRST Kekuléstr. 7 ; 12489 Berlin ; Germany

PID  :  118478

来源: CEUR

【 摘 要 】

Our goal is to estimate causal interactions in multivariate time series. Using vector autore gressive (VAR) models, these can be defined based on nonvanishing coefficients belonging to respective timelagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an ad ditional sparsitypromoting 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 ab sence of a causal relation requires the coefficients for all timelags to become jointly zero. Such behavior can be achieved by means of `1,2norm 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.