English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Inferring deterministic causal relations

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., et al. (2010). Inferring deterministic causal relations. In 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) (pp. 143-150). Corvallis, OR, USA: AUAI Press.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Daniusis, P1, Author           
Janzing, D2, Author           
Mooij, J1, Author           
Zscheischler, J1, Author           
Steudel, B1, 3, Author           
Zhang, K1, Author           
Schölkopf, B1, Author           
Grünwald P. Spirtes, P., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
3Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

Content

show
hide
Free keywords: -
 Abstract: We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

Details

show
hide
Language(s):
 Dates: 2010-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-0-9749039-6-5
URI: http://event.cwi.nl/uai2010/
BibTex Citekey: 6620
 Degree: -

Event

show
hide
Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Place of Event: Catalina Island, CA, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Corvallis, OR, USA : AUAI Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 143 - 150 Identifier: -