Counting Markov Equivalent Directed Acyclic Graphs Consistent with Background Knowledge
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Abstract (arXiv)
A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equivalence class was recently given by Wien"obst, Bannach, and Li'skiewicz (AAAI 2021). In this paper, we consider the more general problem of counting the number of directed acyclic graphs in a Markov equivalence class when the directions of some of the edges are also fixed (this setting arises, for example, when interventional data is partially available). This problem has been shown in earlier work to be complexity-theoretically hard. In contrast, we show that the problem is nevertheless tractable in an interesting class of instances, by establishing that it is ``fixed-parameter tractable''. In particular, our counting algorithm runs in time that is bounded by a polynomial in the size of the graph, where the degree of the polynomial does not depend upon the number of additional edges provided as input.
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References · 6
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
- 2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41
- 2013Graphical Models for Game Theorycited by 21
- 2022Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applicationscited by 9
- 2018Counting and Sampling from Markov Equivalent DAGs Using Clique Treescited by 8
- 2016Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphscited by 4
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