ArXiv

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

2026-07-27 · 17:14 UTC ·Mohammad Fesanghary ·1 min read
Authors
Mohammad Fesanghary
Categories
cs.LG, stat.ME
arXiv
https://arxiv.org/abs/2607.24673v1
PDF
https://arxiv.org/pdf/2607.24673v1

Brief

Causal-TS is an open-source Python library for causal discovery in high-dimensional, nonstationary multivariate time series. It bundles four specialized algorithms (CDNOTS, CDNOTS+, CEDAR, GRACE), wrappers for common baselines, a GPU-accelerated CI-test layer via PyTorch, and a regime-discovery pipeline with pluggable changepoint detectors. The package includes a CLI, synthetic-data tools, optional DoWhy integration, and supports Python 3.10–3.12.

Why it matters

Causal-TS (published 2026-07-27) is an open-source Python library that implements four specialized time-series causal discovery algorithms—CDNOTS, CDNOTS+, CEDAR, and GRACE—and wrappers for GES, Granger, LASSO-VAR, and LGES.

Key details

  • The library provides a unified conditional-independence (CI) test layer with GPU acceleration via PyTorch, a regime-discovery pipeline with pluggable changepoint detectors to run discovery per detected regime, and optional DoWhy integration for causal-effect estimation.
  • Causal-TS is pip-installable, tested on Python 3.10–3.12, includes a CLI and synthetic-data generators, and is available at https://github.com/bloomberg/causal-ts (4-page intro paper).
Source evidence

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.

Comment: 4 page, Intro paper