CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
arXiv:2606.13024v1 Announce Type: cross Abstract: Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-all" paradigm, struggling to capture distribution shifts and dynamic regime changes inherent in real-world time series. This often leads to entangled representations and spurious causal graphs. In this paper, we propose CausalMoE, a billion-scale multimodal Granger causal foundation model that explicitly models patch-level heterogeneity. CausalMoE introduces a Pattern-R
The continuous evolution of foundation models and the increasing complexity of real-world time series data necessitate more sophisticated causal discovery methods.
This development in multimodal foundation models for causal discovery has significant implications for predictive analytics, anomaly detection, and decision-making in complex systems across various industries.
The ability to explicitly model patch-level heterogeneity and distribution shifts in Granger Causal Discovery offers a more robust and accurate understanding of temporal dependencies, reducing spurious causal links.
- · AI/ML researchers
- · Finance sector
- · Healthcare diagnostics
- · Autonomous systems
- · Traditional neural GCD methods
- · Systems reliant on 'one-size-fits-all' models
More accurate causal inference will improve the reliability of AI-driven predictions and interventions.
Enhanced causal models could lead to advancements in drug discovery, climate modeling, and economic forecasting.
Widespread adoption of such sophisticated causal AI could fundamentally alter risk assessment and strategic planning across industries.
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Read at arXiv cs.AI