  Local causal structure learning aims to discover and distinguish direct
causes (parents) and direct effects (children) of a variable of interest from
data. While emerging successes have been made, existing methods need to search
a large space to distinguish direct causes from direct effects of a target
variable T. To tackle this issue, we propose a novel Efficient Local Causal
Structure learning algorithm, named ELCS. Specifically, we first propose the
concept of N-structures, then design an efficient Markov Blanket (MB) discovery
subroutine to integrate MB learning with N-structures to learn the MB of T and
simultaneously distinguish direct causes from direct effects of T. With the
proposed MB subroutine, ELCS starts from the target variable, sequentially
finds MBs of variables connected to the target variable and simultaneously
constructs local causal structures over MBs until the direct causes and direct
effects of the target variable have been distinguished. Using eight Bayesian
networks the extensive experiments have validated that ELCS achieves better
accuracy and efficiency than the state-of-the-art algorithms.
