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:2609.31315v1 Announce Type: cross Abstract: Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direc...
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: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...
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.
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.
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: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.
arXiv:2606. 03227v1 Announce Type: new Abstract: Causal discovery with instantaneous effects in multivariate time series is challenging, as the instantaneous structure must be acyclic.
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 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.
arXiv:2606. 18011v1 Announce Type: cross Abstract: Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships.
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.