Metadata-Version: 2.1
Name: causal-learn
Version: 0.1.2.0
Summary: causal-learn Python Package
Home-page: https://github.com/cmu-phil/causal-learn
Author: 
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# causal-learn: Causal Discovery for Python

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](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:

* 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.

# Install

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](https://pypi.org/project/sqlparse/):

```
pip install causal-learn
```

# Documentation

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


# 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!

