Header logo is

Exploring the causal order of binary variables via exponential hierarchies of Markov kernels

2007

Conference Paper

ei


We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n>=4) binary variables X_1,...,X_n. Our inference principle states that the factorization of the joint probability into conditional probabilities for X_j given X_1,...,X_{j-1} often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule.

Author(s): Sun, X. and Janzing, D.
Book Title: ESANN 2007
Journal: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Pages: 465-470
Year: 2007
Month: April
Day: 0
Publisher: D-Side

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 15th European Symposium on Artificial Neural Networks
Event Place: Brugge, Belgium

Address: Evere, Belgium
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{4456,
  title = {Exploring the causal order of binary variables via exponential hierarchies of Markov kernels},
  author = {Sun, X. and Janzing, D.},
  journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)},
  booktitle = {ESANN 2007},
  pages = {465-470},
  publisher = {D-Side},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Evere, Belgium},
  month = apr,
  year = {2007},
  month_numeric = {4}
}