pygraphviz (might not support the most recent Mac)
Install via PyPI
To use causal-learn, we could install it using pip:
(.venv) $ pip install causal-learn
Install from source
For development version, please kindly refer to our GitHub Repository.
For search methods in causal discovery, there are various running examples in the ‘tests’ directory in our GitHub Repository, such as TestPC.py and TestGES.py.
For the implemented modules, such as (conditional) independent test methods, we provide unit tests for the convenience of developing your own methods.
For the convenience of our community, CMU-CLeaR group maintains a list of benchmark datasets including real-world scenarios and various learning tasks. Please refer to the following links:
https://github.com/cmu-phil/example-causal-datasets (maintained by Joseph Ramsey)
Please feel free to let us know if you have any recommendation regarding causal datasets with high-quality. We are grateful for any effort that benefits the development of causality community.
Team Leaders: Kun Zhang, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Clark Glymour
Coordinators: Yujia Zheng, Biwei Huang, Wei Chen
Shohei Shimizu, Takashi Nicholas Maeda, Takashi Ikeuchi: LiNGAM-based methods.
Madelyn Glymour: several helpers.
Ruibo Tu: Missing-value/test-wise deletion PC.
Wai-Yin Lam: PC.
Biwei Huang: CD-NOD.
Ignavier Ng, Yujia Zheng: Exact search.
Bryan Andrews, Joseph Ramsey: GRaSP.
Joseph Ramsey, Wei Chen, Zhiyi Huang: Evaluations.
Quality control: Yewen Fan, Haoyue Dai, Yujia Zheng, Ignavier Ng, Xiangchen Song