Bayesian Estimation of Intervention Effects // An implementation
of intervention effect estimation for DAGs (directed acyclic
graphs) learned from binary or continuous data. First,
parameters are estimated or sampled for the DAG and then
interventions on each node (variable) are propagated through
the network (do-calculus). Both exact computation (for
continuous data or for binary data up to around 20 variables)
and Monte Carlo schemes (for larger binary networks) are
implemented.