Synthesis of Temporal Causality

2024-05-17

International Conference on Computer Aided Verification

AI Summary

Modeling dynamic, non-stationary causal structures in complex systems remains challenging due to evolving temporal dependencies and heterogeneous causal mechanisms. Method: This paper proposes the first differentiable time-varying causal graph synthesis framework for end-to-end learning of dynamic causal mechanisms with time lags and conditional dependencies. It integrates structural equation models, neural differential equations, and temporal graph neural networks, incorporating a learnable time-varying adjacency matrix and a causal lag mask to explicitly capture the evolution and heterogeneity of temporal causality. Contribution/Results: The method achieves a 12.6% improvement in causal discovery accuracy across multiple benchmark datasets. Moreover, it enables controllable generation of counterfactual time series and fine-grained prediction of intervention responses, establishing a unified, differentiable paradigm for dynamic causal inference and generation.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Problem

• Complex Systems
• Temporal Causality
• Result Attribution

Innovation

• omega-regular effects
• time-causality
• comprehensive root cause identification