Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs

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.

Extended version of paper accepted to the Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-2021)