# A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton

arXiv: https://arxiv.org/abs/2310.04218 · HTML: https://mlsanity.com/paper/2310.04218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [A Transformational Characterization of Equivalent Bayesian Network Structures](https://mlsanity.com/paper/1302.4938/) (2013)
- [Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs](https://mlsanity.com/paper/2012.09679/) (2020)
- [Counting and Sampling from Markov Equivalent DAGs Using Clique Trees](https://mlsanity.com/paper/1802.01239/) (2018)
- [A Single-Exponential Time 2-Approximation Algorithm for Treewidth](https://mlsanity.com/paper/2104.07463/) (2021)
- [Enumerating Markov Equivalence Classes of Acyclic Digraph Models](https://mlsanity.com/paper/1301.2272/) (2013)

## Built on by (most cited first, 1 of 1)

- [Distribution Learning Meets Graph Structure Sampling](https://mlsanity.com/paper/2405.07914/) (2024)

## Abstract (arXiv)

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 ancestors 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 arightarrow b leftarrow 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.