The paper studies uncertainty quantification in federated Granger causality learning, where multiple parties cannot share data but need to infer predictive dependencies across clients. It addresses the challenge that clients observe different features and that cross-client edges must be estimated indirectly through iterative client‑server communication, which propagates both aleatoric and epistemic uncertainty. By deriving closed‑form variance recursions and steady‑state variances, the authors show that initial model‑parameter uncertainty diminishes asymptotically, enabling statistically principled selection of genuine cross‑client dependencies and improving edge recovery in synthetic and real industrial datasets.
arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
The paper reviews modern causal discovery techniques that go beyond conditional independence, highlighting how distributional properties, such as asymmetries, conditional moments, and environmental variation, can identify causal structure. It distinguishes between identification sources, statistical estimators, and computational implementations, and discusses challenges in the presence of latent confounding, feedback, and cyclic models. The authors also outline consistency, uncertainty quantification, and algorithmic guarantees, and provide guidance for comparing methods and a research agenda for future work.
arXiv:2610.28134v1 Announce Type: cross Abstract: Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causa...
The paper introduces a vertical federated learning framework that learns one‑step‑ahead cross‑client temporal interdependencies in decentralized industrial systems with heterogeneous, proprietary models. Each client compresses high‑dimensional sensor data into low‑dimensional latent states via a nonlinear state‑space model, while a central Graph Attention Network (GAT) learns a state‑transition model and derives attention coefficients that reflect cross‑client interactions. The authors analytically link these attention coefficients to the Jacobian of the transition model, proving bounded closeness to a centralized oracle, and validate the approach with synthetic and real‑world experiments showing improved federated root‑cause analysis.
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...
arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.
arXiv:2607. 09695v1 Announce Type: new Abstract: This paper addresses the challenging problem of dynamic feature drift in federated learning, where data distributions evolve across clients and over time -- a common scenario in real-world applications like financial technology.
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:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.
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: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:2604.09309v2 Announce Type: replace Abstract: The Half-Trek Criterion (HTC) is the primary graphical tool for determining generic identifiability of causal effect coefficients in linear structu...