Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
Machine Learning
2023-08-22 v3 Artificial Intelligence
Machine Learning
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
@article{arxiv.2205.02654,
title = {Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications},
author = {Marcel Wienöbst and Max Bannach and Maciej Liśkiewicz},
journal= {arXiv preprint arXiv:2205.02654},
year = {2023}
}
Comments
arXiv admin note: text overlap with arXiv:2012.09679