Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Inference on Discrete Data using Additive Noise Models   Inferring the causal structure of a set of random variables from a finite
sample of the joint distribution is an important problem in science. Recently,
methods using additive noise models have been suggested to approach the case of
continuous variables. In many situations, however, the variables of interest
are discrete or even have only finitely many states. In this work we extend the
notion of additive noise models to these cases. We prove that whenever the
joint distribution $\prob^{(X,Y)}$ admits such a model in one direction, e.g.
$Y=f(X)+N, N \independent X$, it does not admit the reversed model
$X=g(Y)+\tilde N, \tilde N \independent Y$ as long as the model is chosen in a
generic way. Based on these deliberations we propose an efficient new algorithm
that is able to distinguish between cause and effect for a finite sample of
discrete variables. In an extensive experimental study we show that this
algorithm works both on synthetic and real data sets.

33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph