Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
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In this report we describe a tool for comparing the performance of causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java), which can be loaded up in an Integrated Development Environment (IDE) such as IntelliJ IDEA. Simulations can be done varying the number of runs, sample sizes, and data modalities. Performance on this simulated data can then be compared for a number of algorithms, with parameters varied and with performance statistics as selected, producing a publishable report. The order of the algorithms in the output can be adjusted to the user's preference using a utility function over the statistics. Data sets from simulation can be saved along with their graphs to a file and loaded back in for further analysis, or used for analysis by other tools. The package presented here may also be used to compare structure learning methods across platforms and programming languages, i.e., to compare algorithms implemented in TETRAD with those implemented in MATLAB or R.
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