Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Linear Non-Gaussian Causal Models in the Presence of Latent
  Variables   We consider the problem of learning causal models from observational data
generated by linear non-Gaussian acyclic causal models with latent variables.
Without considering the effect of latent variables, one usually infers wrong
causal relationships among the observed variables. Under faithfulness
assumption, we propose a method to check whether there exists a causal path
between any two observed variables. From this information, we can obtain the
causal order among them. The next question is then whether or not the causal
effects can be uniquely identified as well. It can be shown that causal effects
among observed variables cannot be identified uniquely even under the
assumptions of faithfulness and non-Gaussianity of exogenous noises. However,
we will propose an efficient method to identify the set of all possible causal
effects that are compatible with the observational data. Furthermore, we
present some structural conditions on the causal graph under which we can learn
causal effects among observed variables uniquely. We also provide necessary and
sufficient graphical conditions for unique identification of the number of
variables in the system. Experiments on synthetic data and real-world data show
the effectiveness of our proposed algorithm on learning causal models.

8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th