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Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data. Results: We present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent modeldoi:10.1186/1471-2105-8-s2-s3 pmid:17493252 pmcid:PMC1892072 fatcat:ydvthpjkgrhb3ebjj7cbiinwxi