CausationEntropy: Pythonic Optimal Causation Entropy

TL;DR

CausationEntropy is a Python package implementing optimal causation entropy methods for causal network discovery in dynamical systems, featuring new algorithms, estimators, and tools for robust causal inference.

Contribution

This paper introduces version 1.1 of CausationEntropy, a Python package with advanced causal discovery algorithms, new data generators, and estimators, enhancing causal network modeling capabilities.

Findings

Includes new synthetic data generators and plotting tools.

Implements multiple advanced information-theoretical estimators.

Provides a modular, well-documented package for causal discovery.

Abstract

Optimal Causation Entropy (oCSE) is a robust causal network modeling technique that reveals causal networks from dynamical systems and coupled oscillators, distinguishing direct from indirect paths. CausationEntropy is a Python package that implements oCSE and several of its significant optimizations and methodological extensions. In this paper, we introduce the version 1.1 release of CausationEntropy, which includes new synthetic data generators, plotting tools, and several advanced information-theoretical causal network discovery algorithms with criteria for estimating Gaussian, k-nearest neighbors (kNN), geometric k-nearest neighbors (geometric-kNN), kernel density (KDE) and Poisson entropic estimators. The package is easy to install from the PyPi software repository, is thoroughly documented, supplemented with extensive code examples, and is modularly structured to support future additions. The entire codebase is released under the MIT license and is available on GitHub and through PyPi Repository. We expect this package to serve as a benchmark tool for causal discovery in complex dynamical systems.