Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent
实验干预下稀疏因果高斯网络的学习：正则化与坐标下降
Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures, and the presence of equivalence classes. In this article, we develop an L 1-penalized likelihood approach to estimate the structure of causal Gaussian networks. A blockwise coordinate descent algorithm, which takes advantage of the…