🤖 AI Summary
This work addresses the challenge of causal discovery in high-dimensional, nonstationary multivariate time series by introducing an open-source Python library that integrates, for the first time, a unified GPU-accelerated conditional independence testing layer, plug-in structural change-point detection, and multiple causal discovery algorithms—including CDNOTS, GES, Granger causality, and LASSO-VAR—to enable piecewise causal modeling and end-to-end causal effect estimation. Implemented in PyTorch for computational efficiency, the library supports Python 3.10–3.12, offers a command-line interface, and seamlessly integrates with DoWhy. Released publicly on GitHub, this tool significantly enhances the scalability and usability of causal analysis for nonstationary time series data.
📝 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.