Python工具包CausationEntropy实现因果网络建模，助力复杂系统分析。
CausationEntropy: Pythonic Optimal Causation Entropy
- 基于信息论的oCSE算法，区分直接与间接因果路径。
- 支持高斯、kNN、核密度等五种熵估计方法，精度更高。
- 开源易用，适合研究复杂系统与因果发现的科研人员。
最优因果熵（oCSE）是一种稳健的因果网络建模技术，可从动力系统和耦合振子中揭示因果关系，区分直接与间接路径。CausationEntropy是实现oCSE及其多种优化与扩展方法的Python包。本文介绍其1.1版本，新增合成数据生成器、绘图工具，以及针对高斯、k近邻（kNN）、几何k近邻（geometric-kNN）、核密度估计（KDE）和泊松熵估计的先进信息论因果网络发现算法。该包可通过PyPi轻松安装，文档详尽，代码结构模块化，便于扩展。全部代码以MIT许可证发布，可在GitHub和PyPi获取。我们期望此工具成为复杂动力系统因果发现的基准平台。
原文摘要 · Abstract (English)
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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