Simulate data of a causal cyclic model under shift interventions.
simulateInterventions(
n,
p,
A,
G,
intervMultiplier,
noiseMult,
nonGauss,
hiddenVars,
knownInterventions,
fracVarInt,
simulateObs,
seed = 1
)Number of observations.
Number of variables.
Connectivity matrix A. The entry \(A_{ij}\) contains the edge from node i to node j.
Number of environments, has to be larger than two for backShift.
Regulates the strength of the interventions.
Regulates the noise variance.
Set to TRUE to generate non-Gaussian noise.
Set to TRUE to include hidden variables.
Set to TRUE if location of interventions
should be known.
If knownInterventions is TRUE, fraction of
variables that are intervened on in each environment.
If TRUE, also generate observational data.
Random seed.
A list with the following elements:
X (nxp)-dimensional data matrix
environment Indicator of the experiment or the intervention type an
observation belongs to. A numeric vector of length n.
interventionVar (Gxp)-dimensional matrix with intervention variances.
interventions Location of interventions if knownInterventions
was set to TRUE.
configs A list with the following elements:
trueA True connectivity matrix used to generate the data.
G Number of environments.
indexObservationalData Index of observational data
intervMultiplier Multiplier steering the intervention strength
noiseMult Multiplier steering the noise level
fracVarInt If knownInterventions was set to TRUE,
fraction of variables that were intervened on in each environment.
hiddenVars If TRUE, hidden variables exist.
knownInterventions If TRUE, location of interventions is known.
simulateObs If TRUE, environment 1 contains
observational data.
Dominik Rothenhaeusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen (2015): backShift: Learning causal cyclic graphs from unknown shift interventions. arXiv preprint: http://arxiv.org/abs/1506.02494