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https://github.com/py-why/causal-learn

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
https://github.com/py-why/causal-learn

causal causal-discovery causal-inference causal-representation-learning causality confounder graph hidden-causal independence-tests python statistics structure tetrad time-series

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Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

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README 

        
# causal-learn: Causal Discovery in Python



Causal-learn ([documentation](https://causal-learn.readthedocs.io/en/latest/), [paper](https://jmlr.org/papers/volume25/23-0970/23-0970.pdf)) 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](https://github.com/cmu-phil/tetrad).



The package is actively being developed. Feedbacks (issues, suggestions, etc.) are highly encouraged.



# Package Overview



Our causal-learn implements methods for causal discovery:



* Constraint-based causal discovery methods.

* Score-based causal discovery methods.

* Causal discovery methods based on constrained functional causal models.

* Hidden causal representation learning.

* Permutation-based causal discovery methods.

* Granger causality.

* Multiple utilities for building your own method, such as independence tests, score functions, graph operations, and evaluations.



# Install



Causal-learn needs the following packages to be installed beforehand:



* python 3 (>=3.7)

* numpy

* networkx

* pandas

* scipy

* scikit-learn

* statsmodels

* pydot



(For visualization)



* matplotlib

* graphviz



To use causal-learn, we could install it using [pip](https://pypi.org/project/causal-learn/):



```

pip install causal-learn

```



# Documentation



Please kindly refer to [causal-learn Doc](https://causal-learn.readthedocs.io/en/latest/) for detailed tutorials and usages.



# Running examples



For search methods in causal discovery, there are various running examples in the **‘tests’** directory, 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.



# Benchmarks



For the convenience of our community, [CMU-CLeaR](https://www.cmu.edu/dietrich/causality) 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](https://github.com/cmu-phil/example-causal-datasets) (maintained by Joseph Ramsey)

* [https://www.cmu.edu/dietrich/causality/projects/causal_learn_benchmarks](https://www.cmu.edu/dietrich/causality/projects/causal_learn_benchmarks)



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.



# Contribution



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!



# Running Tetrad in Python



Although causal-learn provides python implementations for some causal discovery algorithms, there are currently a lot more in the classical Java-based [Tetrad](https://github.com/cmu-phil/tetrad) program. For users who would like to incorporate arbitrary Java code in Tetrad as part of a Python workflow, we strongly recommend considering [py-tetrad](https://github.com/cmu-phil/py-tetrad). Here is a list of [reusable examples](https://github.com/cmu-phil/py-tetrad/tree/main/pytetrad) of how to painlessly benefit from the most comprehensive Tetrad Java codebase.



# Citation



Please cite as:



```

@article{zheng2024causal,

  title={Causal-learn: Causal discovery in python},

  author={Zheng, Yujia and Huang, Biwei and Chen, Wei and Ramsey, Joseph and Gong, Mingming and Cai, Ruichu and Shimizu, Shohei and Spirtes, Peter and Zhang, Kun},

  journal={Journal of Machine Learning Research},

  volume={25},

  number={60},

  pages={1--8},

  year={2024}

}

```