BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs

TL;DR

The paper introduces BCDAG, an R package that facilitates Bayesian causal discovery and effect estimation for Gaussian DAGs, efficiently scaling with data size and sparsity, with tools for diagnostics and visualization.

Contribution

The paper presents a new R package implementing an MCMC-based Bayesian causal discovery method for Gaussian DAGs, with scalable performance and comprehensive analysis tools.

Findings

Efficient scalability with data size and sparsity.

Successful application on real and simulated datasets.

Provides diagnostics and visualization tools.

Abstract

Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal effects between variables also from pure observational data. In this setting, the process of inferring the DAG structure from the data is referred to as causal structure learning or causal discovery. We introduce BCDAG, an R package for Bayesian causal discovery and causal effect estimation from Gaussian observational data, implementing the Markov chain Monte Carlo (MCMC) scheme proposed by Castelletti & Mascaro (2021). Our implementation scales efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, with the number of variables in the dataset. The package also provides functions for convergence diagnostics and for visualizing and summarizing posterior inference. In this paper, we present the key features of the underlying methodology along with its implementation in BCDAG. We then illustrate the main functions and algorithms on both real and simulated datasets.