# On Separation Criterion and Recovery Algorithm for Chain Graphs

arXiv: https://arxiv.org/abs/1302.3606 · HTML: https://mlsanity.com/paper/1302.3606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2013.

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

- [Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding](https://mlsanity.com/paper/2411.01371/) (2024)

## Abstract (arXiv)

Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structures. In the paper a direct graphical separation criterion for chain graphs, called c-separation, which generalizes the d-separation criterion for Bayesian networks is introduced (recalled). It is equivalent to the classic moralization criterion for chain graphs and complete in sense that for every chain graph there exists a probability distribution satisfying exactly conditional independencies derivable from the chain graph by the c-separation criterion. Every class of Markov equivalent chain graphs can be uniquely described by a natural representative, called the largest chain graph. A recovery algorithm, which on basis of the (conditional) dependency model induced by an unknown chain graph finds the corresponding largest chain graph, is presented.