simulateInterventions {backShift}

Simulate data of a causal cyclic model under shift interventions.

Description

Simulate data of a causal cyclic model under shift interventions.

Usage

simulateInterventions(
  n,
  p,
  A,
  G,
  intervMultiplier,
  noiseMult,
  nonGauss,
  hiddenVars,
  knownInterventions,
  fracVarInt,
  simulateObs,
  seed = 1
)

Arguments

n

Number of observations.

p

Number of variables.

A

Connectivity matrix A. The entry A_{ij} contains the edge from node i to node j.

G

Number of environments, has to be larger than two for backShift.

intervMultiplier

Regulates the strength of the interventions.

noiseMult

Regulates the noise variance.

nonGauss

Set to TRUE to generate non-Gaussian noise.

hiddenVars

Set to TRUE to include hidden variables.

knownInterventions

Set to TRUE if location of interventions should be known.

fracVarInt

If knownInterventions is TRUE, fraction of variables that are intervened on in each environment.

simulateObs

If TRUE, also generate observational data.

seed

Random seed.

Value

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.

References

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