Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parallel ensemble methods for causal direction inference   Inferring the causal direction between two variables from their observation
data is one of the most fundamental and challenging topics in data science. A
causal direction inference algorithm maps the observation data into a binary
value which represents either x causes y or y causes x. The nature of these
algorithms makes the results unstable with the change of data points. Therefore
the accuracy of the causal direction inference can be improved significantly by
using parallel ensemble frameworks. In this paper, new causal direction
inference algorithms based on several ways of parallel ensemble are proposed.
Theoretical analyses on accuracy rates are given. Experiments are done on both
of the artificial data sets and the real world data sets. The accuracy
performances of the methods and their computational efficiencies in parallel
computing environment are demonstrated.

28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an