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

arXiv: https://arxiv.org/abs/1401.5636 · HTML: https://mlsanity.com/paper/1401.5636/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2014.

## Built on by (most cited first, 1 of 1)

- [Binary Independent Component Analysis: A Non-stationarity-based Approach](https://mlsanity.com/paper/2111.15431/) (2021)

## Abstract (arXiv)

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.