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  Identifying Cause and Effect on Discrete Data using Additive Noise Models

Peters, J., Janzing, D., & Schölkopf, B. (2010). Identifying Cause and Effect on Discrete Data using Additive Noise Models. Cambridge, MA, USA: JMLR.

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 Creators:
Peters, J1, 2, Author           
Janzing, D1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.
Whenever the joint distribution P(X;Y ) admits
such a model in one direction, e.g. Y =
f(X) + N; N ? X, it does not admit the
reversed model X = g(Y ) + ~N ; ~N ? 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. We show that
this algorithm works both on synthetic and
real data sets.

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 Dates: 2010-05
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6387
 Degree: -

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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Place of Event: Chia Laguna Resort, Italy
Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
 Creator(s):
Teh, YW, Editor
Titterington , M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 597 - 604 Identifier: -