arXiv AI June 12, 2026 By Bo Liu, Di Dai, Jingwei Liu, Jiarui Jin, Xiaocheng Fang, Guangkun Nie, Hongyan Li, Shenda Hong

CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts

Read the original on arXiv AI →

arXiv:2606. 13024v1 Announce Type: cross Abstract: Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 15

Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

arXiv:2605. 26759v2 Announce Type: replace Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies.

By Biao Ouyang, Tengxue Zhang, Zhihao Zhuang, Yang Shu, Chenjuan Guo, Bin Yang

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv Machine Learning

Jun 26

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.

By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf

arXiv Machine Learning

Jul 31

Causal Discovery with Inverted Self-attention for Multivariate Time Series

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.

By Yusen Liu, Yong Wang, Yifan Yin, Tianqing Zhu, Xiufeng Liu, Huan Huo

llms efficiency benchmarks

arXiv Machine Learning

Jul 31

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.

By S. A. Adedayo

benchmarks

arXiv Machine Learning

Aug 11

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.

By Bowen Lu, Liangqiang Yang, Teng Li, Kun Zhang

diffusion benchmarks

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Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining

FoundCause: Causal Discovery with Latent Confounders from Observational Data

Use What You Know: Causal Foundation Models with Partial Graphs

Causal Discovery with Inverted Self-attention for Multivariate Time Series

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CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization