arXiv:2606. 28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.
arXiv:2606. 28225v2 Announce Type: replace Abstract: Temporal link prediction (TLP) is typically evaluated by predictive performance on unseen edges, but this criterion can conflate predictive accuracy with recovery of the underlying causal mechanism.
arXiv:2609.36689v1 Announce Type: new Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that v...
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.
arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.
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:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gr...
arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...
arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pai...