Causation Entropy Documentation
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:
pip install causationentropy
Basic usage:
from causationentropy 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')
Nice plots:
Our goal is to produce a library that produces nice publication ready plots!
Here is an example:
Please Cite
If you use Causation Entropy in your work, please cite:
@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}
}