CausationEntropy: Pythonic Optimal Causation Entropy
This provides a benchmark tool for causal discovery in complex dynamical systems, but it is incremental as it packages existing methods.
The authors introduced CausationEntropy version 1.1, a Python package implementing Optimal Causation Entropy (oCSE) and its extensions for causal network modeling from dynamical systems, including new data generators, plotting tools, and multiple entropic estimators.
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.