Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Abstract
When learning a directed acyclic graph (DAG) model via observational data, one generally cannot identify the underlying DAG, but can potentially obtain a Markov equivalence class. The size (the number of DAGs) of a Markov equivalence class is crucial to infer causal effects or to learn the exact causal DAG via further interventions. Given a set of Markov equivalence classes, the distribution of their sizes is a key consideration in developing learning methods. However, counting the size of an equivalence class with many vertices is usually computationally infeasible, and the existing literature reports the size distributions only for equivalence classes with ten or fewer vertices.
Cite
Text
He et al. "Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs." Journal of Machine Learning Research, 2015.Markdown
[He et al. "Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs." Journal of Machine Learning Research, 2015.](https://mlanthology.org/jmlr/2015/he2015jmlr-counting/)BibTeX
@article{he2015jmlr-counting,
title = {{Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs}},
author = {He, Yangbo and Jia, Jinzhu and Yu, Bin},
journal = {Journal of Machine Learning Research},
year = {2015},
pages = {2589-2609},
volume = {16},
url = {https://mlanthology.org/jmlr/2015/he2015jmlr-counting/}
}