Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From dependency to causality: a machine learning approach   The relationship between statistical dependency and causality lies at the
heart of all statistical approaches to causal inference. Recent results in the
ChaLearn cause-effect pair challenge have shown that causal directionality can
be inferred with good accuracy also in Markov indistinguishable configurations
thanks to data driven approaches. This paper proposes a supervised machine
learning approach to infer the existence of a directed causal link between two
variables in multivariate settings with $n>2$ variables. The approach relies on
the asymmetry of some conditional (in)dependence relations between the members
of the Markov blankets of two variables causally connected. Our results show
that supervised learning methods may be successfully used to extract causal
information on the basis of asymmetric statistical descriptors also for $n>2$
variate distributions.

15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ