---
title: "Causal-TS: An Open-Source Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.24673v1"
permalink: "https://feed.astralor.com/items/itm_c47e6aba439748552c21be22"
published: "2026-07-27T17:14:48.000Z"
score: 78
channel: "ai"
tags: ["Bloomberg","Causal-TS","CDNOTS","PyTorch","Causal Discovery","Time series analysis"]
---

# Causal-TS: An Open-Source Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

## AI commentary

> This paper is a must-read for anyone working on causal inference with time series. It consolidates scattered algorithms into a unified GPU-accelerated framework and innovatively addresses structural breaks in nonstationary data—a pain point in finance and climate applications. If you're a data scientist or ML engineer, you can pip install and start producing causal graphs right away.

## At a glance

Causal-TS is an open-source Python library from Bloomberg for causal discovery in high-dimensional, nonstationary multivariate time series. It provides four specialized algorithms—CDNOTS, CDNOTS+, CEDAR, and GRACE—alongside wrappers for GES, Granger, LASSO-VAR, and LGES, all sharing a unified conditional independence test layer accelerated by PyTorch on GPU. A regime discovery pipeline detects structural breaks via pluggable changepoint detectors and runs discovery per regime with regime-specific parameters. With a command-line interface, synthetic data generators, and optional DoWhy integration, it offers 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 hosted on GitHub.

## Key points

- Four specialized causal discovery algorithms plus wrappers for classic methods, with a unified CI test layer supporting GPU acceleration.
- Built-in regime discovery pipeline that automatically detects structural breaks and conducts per-regime analysis.
- Integration with DoWhy for end-to-end pipeline from raw time series to causal effect estimation.
- Command-line interface and synthetic data generators to lower the entry barrier.

## Full text

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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