Graphical models, and in particular Bayesian networks, have been widely used
to investigate data in the biological and healthcare domains. This can be
attributed to the recent explosion of high-throughput data across these domains
and the importance of understanding the causal relationships between the
variables of interest. However, classic model validation techniques for
identifying significant edges rely on the choice of an ad-hoc threshold, which
is non-trivial and can have a pronounced impact on the conclusions of the
analysis.

In this paper, we overcome this limitation by proposing simple,
statistically-motivated approach based on L1 approximation for identifying
significant edges. The effectiveness of the proposed approach is demonstrated
on gene expression data sets across two published experimental studies.