Learning Large-Scale Bayesian Networks with the sparsebn Package

TL;DR

The paper introduces sparsebn, an R package designed for scalable learning of large, sparse Bayesian networks from high-dimensional data, including intervention data, with a focus on causal inference and compatibility with existing tools.

Contribution

The paper presents a new R package, sparsebn, optimized for learning large-scale Bayesian networks from high-dimensional data with interventions, emphasizing scalability, consistency, and causal discovery.

Findings

Successfully learns large Bayesian networks from high-dimensional data.

Achieves causal network learning with intervention data.

Compatible with existing network analysis tools.

Abstract

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.