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Discovering causal relations among observed variables in a given data set isa major objective in studies of statistics and artificial intelligence.Recently, some techniques to discover a unique causal model have been exploredbased on non-Gaussianity of the observed data distribution. However, most ofthese are limited to continuous data. In this paper, we present a novel causalmodel for binary data and propose an efficient new approach to deriving theunique causal model governing a given binary data set under skew distributionsof external binary noises. Experimental evaluation shows excellent performancefor both artificial and real world data sets.

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM