English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

Sun, X., Janzing, D., & Schölkopf, B. (2006). Causal Inference by Choosing Graphs with Most Plausible Markov Kernels. In Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006) (pp. 1-11).

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D317-D Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9C39-D
Genre: Conference Paper

Files

show Files
hide Files
:
AIMath-2006-Sun.pdf (Any fulltext), 254KB
Name:
AIMath-2006-Sun.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show
hide
Locator:
http://anytime.cs.umass.edu/aimath06/ (Table of contents)
Description:
-

Creators

show
hide
 Creators:
Sun, X1, 2, Author              
Janzing, D, 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              

Content

show
hide
Free keywords: -
 Abstract: We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called Markov kernels") for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.

Details

show
hide
Language(s):
 Dates: 2006-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5635
 Degree: -

Event

show
hide
Title: Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006)
Place of Event: Fort Lauderdale, FL, USA
Start-/End Date: 2005-01-04 - 2005-01-06

Legal Case

show

Project information

show

Source 1

show
hide
Title: Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 11 Identifier: -