Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Abstract

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.

Synthetic data generation with probabilistic Bayesian Networks

“…Most of the recent work in the field has been aimed at improving the BN modeling scalability, handling mixed data types (including various types of biological data), incorporating latent variables, and developing more robust software interfaces and visualization options (Andrews, Ramsey, and Cooper 2018;Andrews, Ramsey, and Cooper 2019;Chen et al 2019;Hong et al 2018;Jabbari et al 2017;Ogarrio, Spirtes, and J 2016;Raghu et al 2018;Ramsey et al 2017;Sedgewick et al 2016;Spirtes and Zhang 2016;Xing et al 2017;Xing et al 2018;Yu, Liu, and Li 2019;Zhang et al 2018;Zhang et al 2019). In view of these developments, the ability to objectively assess the performance of BN modeling is of the utmost importance.…”