  Discovering causal relations among observed variables in a given data set is
a main topic in studies of statistics and artificial intelligence. Recently,
some techniques to discover an identifiable causal structure 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 a new approach to derive an identifiable
causal structure governing the data based on skew Bernoulli distributions of
external noise. Experimental evaluation shows excellent performance for both
artificial and real world data sets.
