← Back to all news
arXiv Machine Learning By Tatsuya Yamada, Hiroshi Morioka, Yoshinobu Kawahara

Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

Read the original on arXiv Machine Learning →

The paper introduces iCReN, a framework for causal representation learning in time‑series data that jointly models instantaneous and lagged causal relations while handling nonstationarity. It establishes theoretical conditions for identifying latent states and their causal structures up to permutation and component‑wise invertible transformations, using an auxiliary variable linked to changes in transition‑noise distributions. Experiments show that iCReN accurately recovers latent states and both types of causal relations on synthetic data and improves downstream forecasting on real‑world data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 5

From Causal Discovery to Dynamic Causal Inference in Neural Time Series

arXiv:2603. 20980v3 Announce Type: replace Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable.

By Dmitry Zaytsev, Valentina Kuskova, Michael Coppedge
More like this →
arXiv Machine Learning
Jun 8

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606. 07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences.

By Tao Chen, Yexu Zhou, Zhi Gong, Hengwei He, Hongda Li, Zhewei Chen, Dongjing Wang, Xin Zhang, Decheng Liu, Chunlei Peng, Zheng Chen, Wenyue Ding
benchmarks
More like this →
arXiv AI
Sep 7

Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

The paper presents RCBNB-MB, a causal discovery algorithm that relaxes the assumption of a single, time‑consistent causal structure in time series. It identifies latent causal regimes—subsets of time points where a stable causal graph holds—and iteratively segments the series to recover both regime transitions and the corresponding causal graphs using Markov blankets. The authors provide theoretical guarantees and demonstrate through simulations and real IT monitoring data that RCBNB-MB outperforms baseline methods in detecting regime changes and their causal structures.

By Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier
More like this →
arXiv Machine Learning
Jul 21

Causal Discovery on Irregular Time Series

arXiv:2607. 18226v1 Announce Type: new Abstract: Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data.

By Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, M\'ario A. T. Figueiredo, Pedro Bizarro
benchmarks
More like this →