Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

• URL: http://arxiv.org/abs/2607.24673v1
• Date: Mon, 27 Jul 2026 17:14:48 GMT
• Title: Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
• Abstract summary: Causal-TS is an open-source library for causal discovery in high-dimensional and nonstationary time series.<n>It provides four specialized algorithms-CDNOTS, CDNOTS+, CEDAR, and GRACE-along with wrappers for GES, Granger, LGES, and LGES.<n>A regime discovery pipeline detects structural breaks via pluggable changepoint detectors and runs discovery per regime with regime-specific parameters.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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