Tools for Causal Discovery on Observational Data
Compute confusion matrix for comparing two adjacency matrices
Extract adjacency matrix from tpdag, cpdag, tpag or pag object
Convert adjacency matrix to graphNEL object
Compute average degree for adjacency matrix
Compare two tpdag or tskeleton objects
Compute confusion matrix for comparing two adjacency matrices
Test for vanishing partial correlations
Compute confusion matrix for comparing two adjacency matrices
Compute confusion matrix for comparing two adjacency matrices
List of edges in adjacency matrix
Convert essential graph to adjacency matrix
Evaluate adjacency matrix estimation
Evaluate adjacency matrix estimation
Evaluate adjacency matrix estimation
Evaluate adjacency matrix estimation
F1 score
Perform causal discovery using the FCI algorithm
False Discovery Rate
False Omission Rate
G1 score
Gaussian L0 score computed on correlation matrix
Get variables with a specific prefix (character method)
Get variables with a specific prefix (data.frame method)
Convert graphNEL object to adjacency matrix
Check for CPDAG
Check for PDAG
Generate Latex tikz code for plotting a temporal DAG, PDAG or PAG.
Compute maximal number of edges for graph
Number of different DAGs
Number of edges in adjacency matrix
Negative predictive value
Perform causal discovery using the PC algorithm
Plot partial ancestral graph (PAG)
Plot adjacency matrix with order information
Plot temporal partial ancestral graph (TPAG)
Plot temporal partially directed acyclic graph (TPDAG)
Plot temporal skeleton
Plot temporal data generating mechanism
Precision
Convert a matrix of probabilities into an adjacency matrix
Recall
Regression-based information loss test
Structural hamming distance between adjacency matrices
Simulate a random DAG
Simulate Gaussian data according to DAG
Specificity
Make a temporal adjacency matrix
Temporal Bayesian Dirichlet equivalent uniform (Score criterion)
Temporal Bayesian Information Criterion (Score criterion)
Perform causal discovery using the temporal FCI algorithm (TFCI)
Estimate the restricted Markov equivalence class using Temporal Greedy...
Perform causal discovery using the temporal PC algorithm (TPC)
Plot temporal graph via Latex
Various tools for inferring causal models from observational data. The package includes an implementation of the temporal Peter-Clark (TPC) algorithm. Petersen, Osler and Ekstrøm (2021) <doi:10.1093/aje/kwab087>. It also includes general tools for evaluating differences in adjacency matrices, which can be used for evaluating performance of causal discovery procedures.
Useful links