Дата публикации: 24-07-2026 00:00:00

Directed acyclic graphs (DAGs) provide a powerful framework to represent dependence relationships among variables in multivariate settings; in addition, under causal assumptions on the underlying data generating mechanism, they allow for the identification and estimation of causal effects between variables even 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, based on the methodology proposed by Castelletti and Mascaro (2021). We summarize the main features of the underlying method and illustrate its implementation using real and simulated data. Our algorithms scale efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, with the number of variables in the dataset. In addition, the package provides functions for convergence diagnostics and for visualizing and summarizing posterior inference.

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