Learning Causal Cyclic Graphs from Unknown Shift Interventions

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Documentation for package ‘backShift’ version

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backShift Estimate connectivity matrix of a directed graph with linear effects and hidden variables.
bootstrapBackShift Computes a simple model-based bootstrap confidence interval for success of joint diagonalization procedure. The model-based bootstrap approach assumes normally distributed error terms; the parameters of the noise distribution are estimated with maximum likelihood.
computeDiagonalization Computes the matrix Delta Sigma_{c,j} resulting from the joint diagonalization for a given environment (cf. Eq.(7) in the paper). If the joint diagonalization was successful the matrix should be diagonal for all environments $j$.
exampleAdjacencyMatrix Example adjacency matrix
generateA Generates a connectivity matrix A.
metricsThreshold Performance metrics for estimate of connectiviy matrix A.
plotDiagonalization Plots the joint diagonalization. I.e. if it was successful the matrices should all be diagonal.
plotGraphEdgeAttr Plotting function to visualize directed graphs
plotInterventionVars Plots the estimated intervention variances.
simulateInterventions Simulate data of a causal cyclic model under shift interventions.