algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
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Abstract
In this report we describe a tool for comparing the performance of graphical 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). 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 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, Python, or R.Keywords
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This publication has 8 references indexed in Scilit:
- Causal discovery and inference: concepts and recent methodological advancesApplied Informatics, 2016
- Brain network response underlying decisions about abstract reinforcersNeuroImage, 2014
- Causal Discovery for Climate Research Using Graphical ModelsJournal of Climate, 2012
- FUNDAMENTAL TRADE-OFFS GENERATING THE WORLDWIDE LEAF ECONOMICS SPECTRUMEcology, 2006
- Searching for the Causal Structure of a Vector Autoregression*Oxford Bulletin of Economics and Statistics, 2003
- The TETRAD Project: Constraint Based Aids to Causal Model SpecificationMultivariate Behavioral Research, 1998
- Simulation Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD II, EQS, and LISREL ProgramsSociological Methods & Research, 1990
- Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical ModelingJournal of Educational Statistics, 1989