Causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of Tetrad.
The package is actively being developed. Feedbacks (issues, suggestions, etc.) are highly encouraged.
Our causal-learn implements methods for causal discovery:
- Constrained-based causal discovery methods.
- Score-based causal discovery methods.
- Causal discovery methods based on constrained functional causal models.
- Hidden causal representation learning.
- Granger causality.
- Multiple utilities for building your own method, such as independence tests, score functions, graph operations, and evaluations.
Causal-learn needs the following packages to be installed beforehand:
- python 3
- numpy
- networkx
- pandas
- scipy
- scikit-learn
- statsmodels
- pydot
(For visualization)
- matplotlib
- graphviz
To use causal-learn, we could install it using pip:
pip install causal-learn
Please kindly refer to causal-learn Doc for detailed tutorials and usages.
Please feel free to open an issue if you find anything unexpected. And please create pull requests, perhaps after passing unittests in 'tests/', if you would like to contribute to causal-learn. We are always targeting to make our community better!