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Directed Acyclic Graphs (DAGs) provide a powerful framework to model causalrelationships among variables in multivariate settings; in addition, throughthe do-calculus theory, they allow for the identification and estimation ofcausal effects between variables also from pure observational data. In thissetting, the process of inferring the DAG structure from the data is referredto as causal structure learning or causal discovery. We introduce BCDAG, an Rpackage for Bayesian causal discovery and causal effect estimation fromGaussian observational data, implementing the Markov chain Monte Carlo (MCMC)scheme proposed by Castelletti & Mascaro (2021). Our implementation scalesefficiently with the number of observations and, whenever the DAGs aresufficiently sparse, with the number of variables in the dataset. The packagealso provides functions for convergence diagnostics and for visualizing andsummarizing posterior inference. In this paper, we present the key features ofthe underlying methodology along with its implementation in BCDAG. We thenillustrate the main functions and algorithms on both real and simulateddatasets.

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