A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the Same Skeleton
Abstract
Causal DAGs (also known as Bayesian networks) are a popular tool for encoding conditional dependencies between random variables. In a causal DAG, the random variables are modeled as vertices in the DAG, and it is stipulated that every random variable is independent of its non-descendants conditioned on its parents. It is possible, however, for two different causal DAGs on the same set of random variables to encode exactly the same set of conditional dependencies. Such causal DAGs are said to be Markov equivalent, and equivalence classes of Markov equivalent DAGs are known as Markov Equivalent Classes (MECs). Beautiful combinatorial characterizations of MECs have been developed in the past few decades, and it is known, in particular, that all DAGs in the same MEC must have the same skeleton (underlying undirected graph) and v-structures (induced subgraph of the form a->b<-c). These combinatorial characterizations also suggest several natural algorithmic questions. One of these is: given an undirected graph G as input, how many distinct Markov equivalence classes have the skeleton G? Much work has been devoted in the last few years to this and other closely related problems. However, to the best of our knowledge, a polynomial-time algorithm for the problem remains unknown. In this paper, we make progress towards this goal by giving a fixed parameter tractable algorithm for the above problem, with the parameters being the treewidth and the maximum degree of the input graph G. The main technical ingredient in our work is a construction we refer to as shadow, which lets us create a local description of long-range constraints imposed by the combinatorial characterizations of MECs.
BibTeX
@article{Sharma_2024, title={A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the Same Skeleton}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30038}, DOI={10.1609/aaai.v38i18.30038}, abstractNote={Causal DAGs (also known as Bayesian networks) are a popular tool for encoding
conditional dependencies between random variables. In a causal DAG, the random
variables are modeled as vertices in the DAG, and it is stipulated that every
random variable is independent of its non-descendants conditioned on its parents. It
is possible, however, for two different causal DAGs on the same set of random
variables to encode exactly the same set of conditional dependencies. Such
causal DAGs are said to be Markov equivalent, and equivalence classes of
Markov equivalent DAGs are known as Markov Equivalent Classes (MECs).
Beautiful combinatorial characterizations of MECs have been developed in the
past few decades, and it is known, in particular, that all DAGs in the same MEC
must have the same skeleton (underlying undirected graph) and v-structures (induced subgraph of the form a->b<-c). These combinatorial characterizations also suggest several natural algorithmic
questions. One of these is: given an undirected graph G as input, how many
distinct Markov equivalence classes have the skeleton G? Much work has been
devoted in the last few years to this and other closely related problems.
However, to the best of our knowledge, a polynomial-time algorithm for the
problem remains unknown. In this paper, we make progress towards this goal by giving a fixed parameter
tractable algorithm for the above problem, with the parameters being the
treewidth and the maximum degree of the input graph G. The main technical
ingredient in our work is a construction we refer to as shadow,
which lets us create a local description of long-range constraints imposed
by the combinatorial characterizations of MECs.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Sharma, Vidya Sagar}, year={2024}, month={Mar.}, pages={20532-20539} }