Discovering causal structures in binary exclusive-or skew acyclic models
This work addresses a gap in causal discovery for binary data, which is incremental as it extends non-Gaussian methods from continuous to binary domains.
The paper tackles the problem of discovering causal structures from binary data by proposing a novel causal model based on skew Bernoulli distributions of external noise, achieving excellent performance on both artificial and real-world datasets.
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.