DAGintervention {Bestie}

Exact estimation of intervention effects for a single DAG or a chain of sampled DAGs

Description

DAGintervention takes a DAG or a sampled chain of DAGs (for example from the partitionMCMC function of the BiDAG package) and computes the intervention effect of each node on all others. For binary data, this is performed by exhaustively examining all possible binary states. This is exponentially complex in the number of variables which should therefore be limited to around 20 or fewer. For more variables there is a Monte Carlo version DAGinterventionMC instead. For continuous data, the intervention estimation is performed by extracting the edge coefficients from their posterior distribution and using matrix inversion following arXiv:2010.00684. User-defined scores are also supported as long as the DAG parameters are analogous to the BDe/BGe cases, see DAGparameters.

Usage

DAGintervention(incidences, dataParams, sample = TRUE, unrollDBN = TRUE)

Arguments

incidences

a single adjacency matrix of a list of adjacency matrices of sampled DAGs, with entry [i,j] equal to 1 when a directed edge exists from node i to node j

dataParams

the data and parameters used to learn the DAGs derived from the scoreparameters function of the BiDAG package

sample

logical indicating whether to sample the parameters of each node from the posterior (TRUE, default) or to take the expectation (FALSE)

unrollDBN

logical indicating whether to unroll a DBN to a full DAG over all time slices (TRUE, default) or to use the compact representation (FALSE)

Value

a single matrix or a list of matrices containing the full set of intervention effects for each input DAG. Entry [i,j] is the downstream effect on node j of intervening on node i (the difference observed at node j when setting node i to 1 and 0)

See Also

scoreparameters

Examples

scoreParam <- BiDAG::scoreparameters("bde", BiDAG::Asia)
causalmat <- DAGintervention(BiDAG::Asiamat, scoreParam)