Telling cause from effect based on high-dimensional observations

Janzing, D; Hoyer, P; Schölkopf, B

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Telling cause from effect based on high-dimensional observations

MPS-Authors

/persons/resource/persons75626

Janzing, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://dl.acm.org/citation.cfm?id=3104384
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

paper_final_6501[0].pdf
(Any fulltext), 467KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Janzing, D., Hoyer, P., & Schölkopf, B. (2010). Telling cause from effect based on high-dimensional observations. In J. Fürnkranz, & T. Joachims (Eds.), 27th International Conference on Machine Learning (ICML 2010) (pp. 479-486). Madison, WI, USA: Omnipress.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BFA4-9

Abstract

We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.