Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space
This is an incremental improvement for researchers and practitioners in causal inference, addressing scalability issues in large-scale real-world problems.
The study tackled the high time complexity of the PC algorithm for Bayesian network structure learning by using a Tsetlin Machine to constrain the search space, resulting in a considerable reduction in computational time while maintaining competitive accuracy.
The PC algorithm is a widely used method in causal inference for learning the structure of Bayesian networks. Despite its popularity, the PC algorithm suffers from significant time complexity, particularly as the size of the dataset increases, which limits its applicability in large-scale real-world problems. In this study, we propose a novel approach that utilises the Tsetlin Machine (TM) to construct Bayesian structures more efficiently. Our method leverages the most significant literals extracted from the TM and performs conditional independence (CI) tests on these selected literals instead of the full set of variables, resulting in a considerable reduction in computational time. We implemented our approach and compared it with various state-of-the-art methods. Our evaluation includes categorical datasets from the bnlearn repository, such as Munin1, Hepar2. The findings indicate that the proposed TM-based method not only reduces computational complexity but also maintains competitive accuracy in causal discovery, making it a viable alternative to traditional PC algorithm implementations by offering improved efficiency without compromising performance.