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Causal information approach to partial conditioning in multivariate data sets

Daniele Marinazzo UGent, Mario Pellicoro and Sebastiano Stramaglia (2012) COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE.
abstract
When evaluating causal influence from one time series to another in a multivariate data set it is necessary to take into account the conditioning effect of the other variables. In the presence of many variables and possibly of a reduced number of samples, full conditioning can lead to computational and numerical problems. In this paper, we address the problem of partial conditioning to a limited subset of variables, in the framework of information theory. The proposed approach is tested on simulated data sets and on an example of intracranial EEG recording from an epileptic subject. We show that, in many instances, conditioning on a small number of variables, chosen as the most informative ones for the driver node, leads to results very close to those obtained with a fully multivariate analysis and even better in the presence of a small number of samples. This is particularly relevant when the pattern of causalities is sparse.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MODEL, FMRI, TIME-SERIES, GRANGER CAUSALITY
journal title
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Comput. Math. Method Med.
issue title
Methodological Advances in Brain Connectivity
article_number
303601
pages
17 pages
Web of Science type
Article
Web of Science id
000305053900001
JCR category
MATHEMATICAL & COMPUTATIONAL BIOLOGY
JCR impact factor
0.791 (2012)
JCR rank
40/46 (2012)
JCR quartile
4 (2012)
ISSN
1748-670X
DOI
10.1155/2012/303601
project
The integrative neuroscience of behavioral control (Neuroscience)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2069641
handle
http://hdl.handle.net/1854/LU-2069641
date created
2012-03-20 08:32:15
date last changed
2015-06-17 09:59:42
@article{2069641,
  abstract     = {When evaluating causal influence from one time series to another in a multivariate data set it is necessary to take into account the conditioning effect of the other variables. In the presence of many variables and possibly of a reduced number of samples, full conditioning can lead to computational and numerical problems. In this paper, we address the problem of partial conditioning to a limited subset of variables, in the framework of information theory. The proposed approach is tested on simulated data sets and on an example of intracranial EEG recording from an epileptic subject. We show that, in many instances, conditioning on a small number of variables, chosen as the most informative ones for the driver node, leads to results very close to those obtained with a fully multivariate analysis and even better in the presence of a small number of samples. This is particularly relevant when the pattern of causalities is sparse.},
  articleno    = {303601},
  author       = {Marinazzo, Daniele and Pellicoro, Mario and Stramaglia, Sebastiano},
  issn         = {1748-670X},
  journal      = {COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE},
  keyword      = {MODEL,FMRI,TIME-SERIES,GRANGER CAUSALITY},
  language     = {eng},
  pages        = {17},
  title        = {Causal information approach to partial conditioning in multivariate data sets},
  url          = {http://dx.doi.org/10.1155/2012/303601},
  year         = {2012},
}

Chicago
Marinazzo, Daniele, Mario Pellicoro, and Sebastiano Stramaglia. 2012. “Causal Information Approach to Partial Conditioning in Multivariate Data Sets.” Computational and Mathematical Methods in Medicine.
APA
Marinazzo, D., Pellicoro, M., & Stramaglia, S. (2012). Causal information approach to partial conditioning in multivariate data sets. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE.
Vancouver
1.
Marinazzo D, Pellicoro M, Stramaglia S. Causal information approach to partial conditioning in multivariate data sets. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE. 2012;
MLA
Marinazzo, Daniele, Mario Pellicoro, and Sebastiano Stramaglia. “Causal Information Approach to Partial Conditioning in Multivariate Data Sets.” COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2012): n. pag. Print.