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AAAI 2020

Title

Conference Paper AAAI Technical Track: Reasoning under Uncertainty Artificial Intelligence

Abstract

We consider the problem of counting the number of DAGs which are Markov-equivalent, i. e. , which encode the same conditional independencies between random variables. The problem has been studied, among others, in the context of causal discovery, and it is known that it reduces to counting the number of so-called moral acyclic orientations of certain undirected graphs, notably chordal graphs. Our main empirical contribution is a new algorithm which outperforms previously known exact algorithms for the considered problem by a significant margin. On the theoretical side, we show that our algorithm is guaranteed to run in polynomial time on a broad class of chordal graphs, including interval graphs.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
81252082912763662