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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461794.0, MPI für biologische Kybernetik / Biologische Kybernetik
Regression by dependence minimization and its application to causal inference in additive noise models
Authors:Mooij, J.M.; Janzing, D.; Peters, J.; Schölkopf, B.
Editors:Danyluk, A.; Bottou, L.; Littman, M.
Date of Publication (YYYY-MM-DD):2009-06
Title of Proceedings:Proceedings of the 26th International Conference on Machine Learning (ICML 2009)
Start Page:745
End Page:752
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Motivated by causal inference problems, we
propose a novel method for regression that
minimizes the statistical dependence between
regressors and residuals. The key advantage
of this approach to regression is that it does
not assume a particular distribution of the
noise, i.e., it is non-parametric with respect
to the noise distribution. We argue that the
proposed regression method is well suited to
the task of causal inference in additive noise
models. A practical disadvantage is that the
resulting optimization problem is generally
non-convex and can be difficult to solve. Nevertheless,
we report good results on one of the
tasks of the NIPS 2008 Causality Challenge,
where the goal is to distinguish causes from
effects in pairs of statistically dependent variables.
In addition, we propose an algorithm
for efficiently inferring causal models from
observational data for more than two variables.
The required number of regressions
and independence tests is quadratic in the
number of variables, which is a significant improvement
over the simple method that tests
all possible DAGs.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:5869
URL:http://www.cs.mcgill.ca/~icml2009/
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