Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Instrumental Variables as Priors in Causal Inference based on
  Independence of Cause and Mechanism   Causal inference methods based on conditional independence construct Markov
equivalent graphs, and cannot be applied to bivariate cases. The approaches
based on independence of cause and mechanism state, on the contrary, that
causal discovery can be inferred for two observations. In our contribution, we
challenge to reconcile these two research directions. We study the role of
latent variables such as latent instrumental variables and hidden common causes
in the causal graphical structures. We show that the methods based on the
independence of cause and mechanism, indirectly contain traces of the existence
of the hidden instrumental variables. We derive a novel algorithm to infer
causal relationships between two variables, and we validate the proposed method
on simulated data and on a benchmark of cause-effect pairs. We illustrate by
our experiments that the proposed approach is simple and extremely competitive
in terms of empirical accuracy compared to the state-of-the-art methods.

12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph