arXiv:2507. 12257v4 Announce Type: replace Abstract: Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science.
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.
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. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions.
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.
arXiv:2607. 28212v1 Announce Type: cross Abstract: Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables.
arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...
The paper introduces TS‑BOSS, a time‑series extension of the Best Order Score Search (BOSS) algorithm for causal structure learning. TS‑BOSS conducts a permutation‑based search over dynamic Bayesian network structures, using grow‑shrink trees to cache intermediate score computations, thereby maintaining scalability and strong empirical performance. The authors provide theoretical guarantees of soundness under suitable assumptions and demonstrate that TS‑BOSS achieves higher adjacency recall than standard constraint‑based methods, especially in high auto‑correlation regimes.
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.
arXiv:2607. 24673v1 Announce Type: new Abstract: We describe Causal-TS, an open-source Python library for causal discovery in high-dimensional and nonstationary multivariate time series.
arXiv:2609.23516v1 Announce Type: new Abstract: Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become m...
The paper investigates root cause analysis for anomalies in linear time-series data by using difference graph discovery. It focuses on effect-defying root causes—variables whose causal coefficients differ between normal and anomalous regimes—within linear discrete-time dynamic structural causal models. The authors adapt existing difference graph methods to the time-series context, evaluate them on simulated data, and apply them to real-world IT and intensive care monitoring datasets to localize causal mechanisms behind anomalous behavior.
arXiv:2607. 20696v1 Announce Type: new Abstract: We propose CEDAR (Causal Edge Discovery for Autoregressive Processes), a constraint-based method for lagged causal edge discovery in sparse autoregressive 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.