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Faster Fixed Parameter Tractable Algorithms for Counting Markov Equivalence Classes with Special Skeletons (2312.17626v1)

Published 29 Dec 2023 in cs.DS

Abstract: The structure of Markov equivalence classes (MECs) of causal DAGs has been studied extensively. A natural question in this regard is to algorithmically find the number of MECs with a given skeleton. Until recently, the known results for this problem were in the setting of very special graphs (such as paths, cycles, and star graphs). More recently, a fixed-parameter tractable (FPT) algorithm was given for this problem which, given an input graph $G$, counts the number of MECs with the skeleton $G$ in $O(n(2{O(d4k4)} + n2))$ time, where $n$, $d$, and $k$, respectively, are the numbers of nodes, the degree, and the treewidth of $G$. We give a faster FPT algorithm that solves the problem in $O(n(2{O(d2k2)} + n2))$ time when the input graph is chordal. Additionally, we show that the runtime can be further improved to polynomial time when the input graph $G$ is a tree.

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References (13)
  1. A Characterization of Markov Equivalence Classes for Acyclic Digraphs. Annals of Statistics, 25(2):505–541, 1997.
  2. J. R. S. Blair and B. Peyton. An introduction to Chordal Graphs and Clique Trees. In Graph Theory and Sparse Matrix Computation, pages 1–29. Springer, 1993.
  3. S. B. Gillispie. Formulas for counting acyclic digraph markov equivalence classes. Journal of Statistical Planning and Inference, 136(4):1410–1432, 2006.
  4. The Size Distribution for Markov Equivalence Classes of Acyclic Digraph Models. Artificial Intelligence, 141(1-2):137–155, 2002.
  5. C. Meek. Causal Inference and Causal Explanation with Background Knowledge. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI 1995), pages 403–410, 1995.
  6. M. D. Perlman. Graphical Model Search via Essential Graphs. Contemporary Mathematics, 287:255–266, 2001.
  7. Counting Markov Equivalence Classes by Number of Immoralities. arXiv:1611.07493, 2016.
  8. Counting markov equivalence classes for dag models on trees. Discrete Applied Mathematics, 244:170–185, 2018.
  9. Algorithmic Aspects of Vertex Elimination on Graphs. SIAM Journal on Computing, 5(2):266–283, 1976.
  10. D. Schmid and A. Sly. On the number and size of markov equivalence classes of random directed acyclic graphs. arXiv preprint arXiv:2209.04395, 2022.
  11. V. S. Sharma. A fixed-parameter tractable algorithm for counting markov equivalence classes with the same skeleton, 2023.
  12. B. Steinsky. Enumeration of labelled chain graphs and labelled essential directed acyclic graphs. Discrete mathematics, 270(1-3):267–278, 2003.
  13. T. Verma and J. Pearl. Equivalence and synthesis of causal models. In Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, pages 255–270, 1990.
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