  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 \emph{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 \emph{T} and simultaneously distinguish direct causes from direct effects of
\emph{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.
