Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs
Machine Learning
2020-12-18 v1 Artificial Intelligence
Machine Learning
Abstract
Counting and uniform sampling of directed acyclic graphs (DAGs) 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. Experimental results show that the algorithms significantly outperform state-of-the-art methods.
Cite
@article{arxiv.2012.09679,
title = {Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs},
author = {Marcel Wienöbst and Max Bannach and Maciej Liśkiewicz},
journal= {arXiv preprint arXiv:2012.09679},
year = {2020}
}
Comments
Extended version of paper accepted to the Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-2021)