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 test layer with GPU acceleration via PyTorch.
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.
The Regime-Enhanced Intelligent Granger Network is proposed, a novel causal discovery framework specifically engineered for nonstationary time series and achieves an Optimal F1 score of 0.529, substantially outperforming PCMCI+ and PC in structural edge recovery.
Quang-Vinh Dang, M. Dinh, Dat Le et al.· Journal of Data Science and...· 0 citations
Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder adjustment, data-driven lag selection, and inference that controls the false discovery rate (FDR). ORACLE-VARX does all three in one...
Hong-Qiong Tan, Isaac-Neil Zanoria, James Chen et al.· 0 citations
Unveiling directional causality in complex, high-dimensional dynamical systems remains a fundamental challenge. Conventional nonparametric approaches, such as Convergent Cross Mapping, require computationally intensive surrogate tests and frequently yield false-positive detections under strong coupling due to generaliz...
Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series under a linear model. I...
Wen-Bo Xu, Yue He, Yun-Hai Wang et al.· 0 citations
A regime-adaptive deconfounding layer that first estimates the confounding regime from data using a Mar\v{c}enko--Pastur spectral router, then applies a deconfounding strategy matched to that regime, which attenuates factor-dominated variation and recovers contemporaneous structure from the resulting innovations.
Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.
Lei Zan, C. Assaad, Emilie Devijver et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
