Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Abstract
Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. Our algorithms are effective and easily implementable. As we show in experiments, these breakthroughs make thought-to-be-infeasible strategies in active learning of causal structures and causal effect identification with regard to a Markov equivalence class practically applicable.
Cite
Text
Wienöbst et al. "Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications." Journal of Machine Learning Research, 2023.Markdown
[Wienöbst et al. "Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications." Journal of Machine Learning Research, 2023.](https://mlanthology.org/jmlr/2023/wienobst2023jmlr-polynomialtime/)BibTeX
@article{wienobst2023jmlr-polynomialtime,
title = {{Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications}},
author = {Wienöbst, Marcel and Bannach, Max and Liśkiewicz, Maciej},
journal = {Journal of Machine Learning Research},
year = {2023},
pages = {1-45},
volume = {24},
url = {https://mlanthology.org/jmlr/2023/wienobst2023jmlr-polynomialtime/}
}