Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian network structure learning with causal effects in the presence
  of latent variables   Latent variables may lead to spurious relationships that can be
misinterpreted as causal relationships. In Bayesian Networks (BNs), this
challenge is known as learning under causal insufficiency. Structure learning
algorithms that assume causal insufficiency tend to reconstruct the ancestral
graph of a BN, where bi-directed edges represent confounding and directed edges
represent direct or ancestral relationships. This paper describes a hybrid
structure learning algorithm, called CCHM, which combines the constraint-based
part of cFCI with hill-climbing score-based learning. The score-based process
incorporates Pearl s do-calculus to measure causal effects and orientate edges
that would otherwise remain undirected, under the assumption the BN is a linear
Structure Equation Model where data follow a multivariate Gaussian
distribution. Experiments based on both randomised and well-known networks show
that CCHM improves the state-of-the-art in terms of reconstructing the true
ancestral graph.

5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph