Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
  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. 
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro   Yamamoto, Yoshinobu Kawahara
stat.ML cs.LG