A Comparison of Public Causal Search Packages on Linear, Gaussian Data
  with No Latent Variables
  We compare Tetrad (Java) algorithms to the other public software packages BNT
(Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of
recovering DAG structure to the extent possible from data generated recursively
from linear, Gaussian structure equation models (SEMs) with no latent
variables, for random graphs, with no additional knowledge of variable order or
adjacency structure, and without additional specification of intervention
information. Each one of the above packages offers at least one implementation
suitable to this purpose. We compare them on adjacency and orientation accuracy
as well as time performance, for fixed datasets. We vary the number of
variables, the number of samples, and the density of graph, for a total of 27
combinations, averaging all statistics over 10 runs, for a total of 270
datasets. All runs are carried out on the same machine and on their native
platforms. An interactive visualization tool is provided for the reader who
wishes to know more than can be documented explicitly in this report.
