Package to test causal discovery algorithm on simulated and real data
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- NBCB: https://link.springer.com/chapter/10.1007/978-3-030-86486-6_28
- PCTMI:
- TiMINO: https://proceedings.neurips.cc/paper/2013/file/47d1e990583c9c67424d369f3414728e-Paper.pdf
- VarLiNGAM: https://www.jmlr.org/papers/volume11/hyvarinen10a/hyvarinen10a.pdf
- PCMCICMIknn: http://proceedings.mlr.press/v124/runge20a/runge20a.pdf
- PCMCIParCorr: http://proceedings.mlr.press/v124/runge20a/runge20a.pdf
- oCSE: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.474.6986&rep=rep1&type=pdf
- tsFCI:
- DyNoTears: https://arxiv.org/pdf/2002.00498.pdf
- GrangerMV: https://pubmed.ncbi.nlm.nih.gov/20481753/
- GrangerPW
- TCDF:
Some algorithms are imported from other langauges such as R and Java
python3 test_simulated_data.py method structure n_samples num_processor verbose
- method: causal dicovery algorithms, choose from [GrangerPW, GrangerMV, TCDF, PCMCICMIknn, PCMCIParCorr, oCSE, PCTMI, tsFCI, VarLiNGAM, TiMINO, Dynotears]
- structure: causal structure, choose from [fork, v_structure, diamond, 7ts2h]
- n_samples: number of timestamps
- num_processor: number of processors
Example: python3 test_fmri.py "NBCB" "fork" 1000 1 1
python3 test_simulated_data.py method num_processor verbose
Example: python3 test_fmri.py "NBCB" 1 1