Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
This work addresses a gap in causal discovery for binary data, which is incremental as it extends existing non-Gaussian methods from continuous to binary domains.
The paper tackles the problem of discovering unique causal models from binary data by introducing a novel causal model and an efficient approach for binary exclusive-or skew acyclic models, showing excellent performance in experiments on artificial and real-world datasets.
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.