CausationEntropy: Pythonic Optimal Causation Entropy
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Abstract (arXiv)
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.
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References · 8
- 2017Detecting causal associations in large nonlinear time series datasetscited by 240
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
- 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
- 2019How Entropic Regression Beats the Outliers Problem in Nonlinear System Identificationcited by 14
- 2017Geometric k-nearest neighbor estimation of entropy and mutual informationcited by 7
- 2020Entropic Causal Inference for Neurological Applicationscited by 5
- 2023Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverycited by 4
- 2020Interaction Networks from Discrete Event Data by Poisson Multivariate Mutual Information Estimation and Information Flow with Applications from Gene Expression Datacited by 2
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