.. Causation Entropy documentation master file =============================== Causation Entropy Documentation =============================== .. image:: _static/images/logo.jpeg :alt: Causation Entropy Logo :width: 200px :align: center Welcome to the Causation Entropy documentation! This library provides tools for analyzing causal relationships using information-theory based methods. .. note:: This is an active project. Check our `GitHub repository `_ for the latest updates. Quick Start ----------- Install the package: .. code-block:: bash pip install causationentropy Basic usage: .. code-block:: python from causationentropy.core.discovery import discover_network import numpy as np # Generate synthetic data data = np.random.randn(100, 5) # 100 time points, 5 variables # Discover causal network network = discover_network(data, method='standard', information='gaussian') .. toctree:: :maxdepth: 2 :caption: User Guide :hidden: tutorials/index .. toctree:: :maxdepth: 2 :caption: API Reference :hidden: api/network_discovery api/information api/datasets api/linalg api/plotting api/stats .. toctree:: :maxdepth: 2 :caption: Theory :hidden: theory/index .. toctree:: :maxdepth: 1 :caption: Links :hidden: GitHub Repository PyPI Package Please Cite ----------- If you use Causation Entropy in your work, please cite: .. code-block:: bibtex @misc{slote2025causationentropy, author = {Slote, Kevin and Fish, Jeremie and Bollt, Erik}, title = {CausationEntropy: A Python Library for Causal Discovery}, url = {https://github.com/Center-For-Complex-Systems-Science/causationentropy}, doi = {10.5281/zenodo.17047565} } Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`