InvarGC: Invariant Granger Causality for Heterogeneous Interventional Time Series under Latent Confounding
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Ziyi, Ren, Shaogang, Qian, Xiaoning, Duffield, Nick |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
by: Zhang, Ziyi, et al.
Published: (2024)
by: Zhang, Ziyi, et al.
Published: (2024)
Towards Invariant Time Series Forecasting in Smart Cities
by: Zhang, Ziyi, et al.
Published: (2024)
by: Zhang, Ziyi, et al.
Published: (2024)
Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
by: Hahn, Maximilian, et al.
Published: (2026)
by: Hahn, Maximilian, et al.
Published: (2026)
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
by: Liu, Meiliang, et al.
Published: (2025)
by: Liu, Meiliang, et al.
Published: (2025)
Relational Causal Discovery with Latent Confounders
by: Negro, Matteo, et al.
Published: (2025)
by: Negro, Matteo, et al.
Published: (2025)
Time Series Domain Adaptation via Latent Invariant Causal Mechanism
by: Cai, Ruichu, et al.
Published: (2025)
by: Cai, Ruichu, et al.
Published: (2025)
Granger Causality Detection with Kolmogorov-Arnold Networks
by: Lin, Hongyu, et al.
Published: (2024)
by: Lin, Hongyu, et al.
Published: (2024)
Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
by: Ma, Pingchuan, et al.
Published: (2024)
by: Ma, Pingchuan, et al.
Published: (2024)
Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
by: Okita, Tsuyoshi
Published: (2026)
by: Okita, Tsuyoshi
Published: (2026)
Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
by: Sookkongwaree, Chakattrai, et al.
Published: (2025)
Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
by: Okita, Tsuyoshi
Published: (2026)
by: Okita, Tsuyoshi
Published: (2026)
Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)
by: Jung, Yonghan, et al.
Published: (2026)
dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data
by: Ribeiro, Adèle H., et al.
Published: (2025)
by: Ribeiro, Adèle H., et al.
Published: (2025)
Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
by: Shao, Daqian, et al.
Published: (2025)
by: Shao, Daqian, et al.
Published: (2025)
Dynamic Incremental Optimization for Best Subset Selection
by: Ren, Shaogang, et al.
Published: (2024)
by: Ren, Shaogang, et al.
Published: (2024)
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
by: Liu, Zehao, et al.
Published: (2025)
by: Liu, Zehao, et al.
Published: (2025)
A Full DAG Score-Based Algorithm for Learning Causal Bayesian Networks with Latent Confounders
by: Gonzales, Christophe, et al.
Published: (2024)
by: Gonzales, Christophe, et al.
Published: (2024)
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
by: Xu, Hangtong, et al.
Published: (2023)
by: Xu, Hangtong, et al.
Published: (2023)
Meaningful Causal Aggregation and Paradoxical Confounding
by: Zhu, Yuchen, et al.
Published: (2023)
by: Zhu, Yuchen, et al.
Published: (2023)
Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions
by: Ma, Nicole, et al.
Published: (2026)
by: Ma, Nicole, et al.
Published: (2026)
Confounded Causal Imitation Learning with Instrumental Variables
by: Zeng, Yan, et al.
Published: (2025)
by: Zeng, Yan, et al.
Published: (2025)
Causal Bayesian Optimization via Exogenous Distribution Learning
by: Ren, Shaogang, et al.
Published: (2024)
by: Ren, Shaogang, et al.
Published: (2024)
CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series
by: Castri, Luca, et al.
Published: (2024)
by: Castri, Luca, et al.
Published: (2024)
Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
by: Job, Simi, et al.
Published: (2026)
by: Job, Simi, et al.
Published: (2026)
LightSAE: Parameter-Efficient and Heterogeneity-Aware Embedding for IoT Multivariate Time Series Forecasting
by: Ren, Yi, et al.
Published: (2025)
by: Ren, Yi, et al.
Published: (2025)
Test-Time Learning of Causal Structure from Interventional Data
by: Chen, Wei, et al.
Published: (2026)
by: Chen, Wei, et al.
Published: (2026)
Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
by: Qian, Linglong, et al.
Published: (2024)
by: Qian, Linglong, et al.
Published: (2024)
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
by: Wang, Danyang, et al.
Published: (2024)
by: Wang, Danyang, et al.
Published: (2024)
time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
by: Sanyal, Debdeep, et al.
Published: (2025)
by: Sanyal, Debdeep, et al.
Published: (2025)
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)
by: Schröder, Maresa, et al.
Published: (2023)
TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences
by: Liu, Yuequn, et al.
Published: (2023)
by: Liu, Yuequn, et al.
Published: (2023)
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
by: Luo, Kang, et al.
Published: (2024)
by: Luo, Kang, et al.
Published: (2024)
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
by: Cai, Ruichu, et al.
Published: (2025)
by: Cai, Ruichu, et al.
Published: (2025)
FlowState: Sampling Rate Invariant Time Series Forecasting
by: Graf, Lars, et al.
Published: (2025)
by: Graf, Lars, et al.
Published: (2025)
Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
by: Chi, Jinjin, et al.
Published: (2026)
by: Chi, Jinjin, et al.
Published: (2026)
A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)
by: Yin, Yujia, et al.
Published: (2025)
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
by: Cheng, Debo, et al.
Published: (2024)
by: Cheng, Debo, et al.
Published: (2024)
Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)
by: You, Zinuo, et al.
Published: (2026)
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
by: Piao, Xihao, et al.
Published: (2026)
by: Piao, Xihao, et al.
Published: (2026)
Causal Identification in Time Series Models
by: Jahn, Erik, et al.
Published: (2025)
by: Jahn, Erik, et al.
Published: (2025)
Similar Items
-
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
by: Zhang, Ziyi, et al.
Published: (2024) -
Towards Invariant Time Series Forecasting in Smart Cities
by: Zhang, Ziyi, et al.
Published: (2024) -
Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
by: Hahn, Maximilian, et al.
Published: (2026) -
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
by: Liu, Meiliang, et al.
Published: (2025) -
Relational Causal Discovery with Latent Confounders
by: Negro, Matteo, et al.
Published: (2025)