Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Abstract
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to discover a unique causal model have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for binary data and propose an efficient new approach to deriving the unique causal model governing a given binary data set under skew distributions of external binary noises. Experimental evaluation shows excellent performance for both artificial and real world data sets.
Cite
@article{arxiv.1401.5636,
title = {Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM},
author = {Takanori Inazumi and Takashi Washio and Shohei Shimizu and Joe Suzuki and Akihiro Yamamoto and Yoshinobu Kawahara},
journal= {arXiv preprint arXiv:1401.5636},
year = {2014}
}
Comments
10 pages. A longer version of our UAI2011 paper (Inazumi et al., 2011). arXiv admin note: text overlap with arXiv:1202.3736