Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Efficient Local Causal Structure Learning   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.

6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
2
The categories of this paper are: math
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph