The dual PC algorithm is a novel scheme to carry out the
conditional independence tests within the PC algorithm for Gaussian data, by leveraging the
inverse relationship between covariance and precision matrices. The algorithm exploits block
matrix inversions on the covariance and precision matrices to simultaneously perform tests on
partial correlations of complementary (or dual) conditioning sets. Simulation studies indicate
that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time
and in recovering the underlying network structure.