SpatioTemporalCausalLearning

A modularized repository for Spatio-Temporal Causal Learning under

• ADNI (https://adni.loni.usc.edu/)
• IRMA (https://www.cell.com/supplemental/S0092-8674(09)00156-1)
• DREAM3 (https://gnw.sourceforge.net/dreamchallenge.html#dream3challenge)
• Netsim (https://www.fmrib.ox.ac.uk/datasets/netsim/)
• VAR
• Lorenz96

Running

Put ADNI data folder outside this repository like the structure below:

├── data
│   ├── ADNI
│   │   ├── CN
│   │   ├── ...
│   │   └── label.csv
│   ├── IRMA
│   ├── ...
│   └── Netsim
└── SpatioTemporalCausalLearning

Example: Run NOTEARS

cd SpatioTemporalCausalLearning/Baselines/NOTEARS
mkdir ECNs_results
nohup python main.py

Contained Reproducible Baselines

• [NOTEARS] Zheng X, Aragam B, Ravikumar P K, et al. Dags with no tears: Continuous optimization for structure learning[J]. Advances in Neural Information Processing Systems, 2018, 31. https://github.com/xunzheng/notears

• [DAGGNN] Yu Y, Chen J, Gao T, et al. DAG-GNN: DAG structure learning with graph neural networks[C]. International Conference on Machine Learning. PMLR, 2019: 7154-7163. https://github.com/fishmoon1234/DAG-GNN

• [NRI] Kipf T, Fetaya E, Wang K C, et al. Neural relational inference for interacting systems[C]. International Conference on Machine Learning. PMLR, 2018: 2688-2697. https://github.com/ethanfetaya/NRI