# 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.

## Key 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.