开源工具包，用于高维非平稳时间序列的因果发现。
Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
- 四类专用算法+多种经典方法集成，支持GPU加速
- 可检测结构突变点并分段进行因果推断
- 适合需要处理复杂时间数据的科研与金融从业者
我们介绍 Causal-TS，一个开源的 Python 工具库，用于高维非平稳多变量时间序列的因果发现。该库包含四种专用算法（CDNOTS、CDNOTS+、CEDAR、GRACE）以及 GES、Granger、LASSO-VAR、LGES 的封装，共享统一的条件独立性检验层，并通过 PyTorch 实现 GPU 加速。其制度发现流程利用可插拔的变点检测器识别结构断裂点，针对每个制度区间使用特定参数运行因果发现。提供命令行接口、合成数据生成器及可选 DoWhy 集成，实现从原始时间序列到因果效应估计的端到端流程。该库支持 pip 安装，兼容 Python 3.10–3.12，项目地址为 https://github.com/bloomberg/causal-ts。
原文摘要 · Abstract (English)
We describe Causal-TS, an open-source Python library for causal discovery in high-dimensional and nonstationary multivariate time series. Causal-TS provides four specialized algorithms-CDNOTS, CDNOTS+, CEDAR, and GRACE-along with wrappers for GES, Granger, LASSO-VAR, and LGES, all sharing a unified conditional independence (CI) test layer with GPU acceleration via PyTorch. A regime discovery pipeline detects structural breaks via pluggable changepoint detectors and runs discovery per regime with regime-specific parameters. A command-line interface, synthetic data generators, and optional DoWhy integration provide an end-to-end pipeline from raw time series to causal effect estimates. The library is pip-installable, tested on Python 3.10--3.12, and available at https://github.com/bloomberg/causal-ts.
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