| Title | : | A Fixed-Parameter Tractable Algorithm for Counting Markov Equivalence Classes with the same Skeleton |
| Speaker | : | Vidya Sagar Sharma (Ph.D student, TIFR) |
| Details | : | Tue, 30 Apr, 2024 11:00 AM @ SSB-334 |
| 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 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). An MEC is graphically represented by the union of DAGs it contains. 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 the talk, we discuss a fixed parameter tractable algorithm for the above problem, with the parameters being the treewidth and the maximum degree of the input graph G. |
