Materials, code, datasets, etc., for the GRaSP project
The contents of this repository are for this paper:
Lam, W. Y., Andrews, B., & Ramsey, J.. (2022). Greedy Relaxations of the Sparsest Permutation Algorithm. Uncertainty in Artificial Intelligence, Accepted.
On arXiv: https://arxiv.org/abs/2206.05421
Contact information:
Wai-yin (Wayne) Lam: [email protected], Bryan Andrews: [email protected], Joseph Ramsey: [email protected]
A scalable Python translation of GRaSP-2 for the linear, Gaussian case is available in the causal-learn package, here:
https://github.com/cmu-phil/causal-learn
https://github.com/cmu-phil/causal-cmd-python-wrapper
All of the simulation dataset used in our paper are available on Box here:
https://cmu.box.com/s/h2e6ab9a62u2yu7kytg5ejsbyijsoc1u
This includes all dataset in tabular form plus all true graphical models for the dataset to compare results to. We include tabular data (instead of covaraince matrices) because we realize some tools require it.
All simulation results reported in the figures in our paper may be downloaded from this GitHub site; please download this file:
https://github.com/cmu-phil/grasp/blob/main/files/grasp_results.zip
This includes all specific algorithms results for all runs in our paper, plus the summaries of these results in the Comparison.txt files, comparing to the true DAG. We also include summaries comparing to the true CPDAG, here:
The latter information is what is plotted in our simulation Figures.
All of these results were generated using the algcomparison facility in Tetrad--
Ramsey, J. D., Malinsky, D., & Bui, K. V. (2020). algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD. Journal of Machine Learning Research, 21(238), 1-6.
The 4-node ground truth models may be downloaded from their source as referenced in our paper. The 5-node path-canceling models in Appendix F may be downloaded from this repository in this file:
https://github.com/cmu-phil/grasp/blob/main/files/udags5.zip
A number of real datasets have been formatted with ground truth for easy analysis using causal discovery algorithms in this GitHub repository
https://github.com/cmu-phil/example-causal-datasets
Users are encouraged to analyze these dataset using own algorithms.
A slide presentation for these may be downloaded from this repository, here:
https://github.com/cmu-phil/grasp/blob/main/files/SomeRealDataExamplesForGrasp.pdf
GRaSP-0 and GRaSP-1 results will be added to these slides.
All Java code for our paper is included in the granch 'grasp-pub' of the GitHub repository for the Tetrad project,
https://github.com/cmu-phil/tetrad
The specific code used to generate the examples for our paper is in this branch:
https://github.com/cmu-phil/tetrad/tree/grasp-pub
The GRaSP class in the granch 'grasp-pub' is here:
The underlying scoring class in the branch 'grasp-pub' is here:
Some other classes in this branch are also used, as referenced in the import statements of the above classes.
Tetrad uses Maven to build the project, which is easily set up in the IntelliJ app, nere:
https://www.jetbrains.com/idea/
Instructions for setting up the project in IntelliJ may be found in the Wiki for the Tetrad project, here:
https://github.com/cmu-phil/tetrad/wiki/Setting-up-Tetrad-in-IntelliJ-IDEA
The Tetrad app build used to generate the examples above is in the Maven Cental Repository and may be downloaded using this link:
To launch the Tetrad app, install a Java JDK (default version 1.8), then type in a terminal window:
java -Xmx[#gigbates-ram]g -jar [path-to-the-above-jar-file].jar
This should work on all platforms; if not, let us know.