Bayesian Structure and Causal Learning of Gaussian Directed Graphs
Transform adjacency matrix into graphNEL object
Compute causal effects between variables
Credible Intervals for bcdagCE Object
Estimate total causal effects from the MCMC output
MCMC diagnostics
Compute posterior probabilities of edge inclusion from the MCMC output
Compute the maximum a posteriori DAG model from the MCMC output
Compute the median probability DAG model from the MCMC output
Enumerate all neighbors of a DAG
MCMC scheme for Gaussian DAG posterior inference
bcdag object plot
bcdagCE object plot
bcdag object print
bcdagCE object print
Generate a Directed Acyclic Graph (DAG) randomly
Random samples from a compatible DAG-Wishart distribution
bcdag object summaries
bcdagCE object summary
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.