Structural Gating and Effect-aligned Lag-resolved Temporal Causal Discovery Framework with Application to Heat-Pollution Extremes

2026-04-11

AI Summary

This study addresses the challenge of accurately identifying lagged causal relationships in complex multivariate time series by proposing the SGED-TCD framework. Integrating, for the first time, a structural gating mechanism, stability-oriented learning, perturbation-effect alignment, and a unified graph extraction strategy, the framework enables interpretable, robust, and functionally consistent inference of lagged causal graphs. It explicitly models dominant lag orders and causal weights, reconstructing hierarchical causal networks to significantly enhance physical interpretability and generalization capability. Applied to compound climate–environment extreme events, the method successfully uncovers heterogeneous driving mechanisms behind heatwave–pollution episodes during warm seasons in eastern China and cold seasons in northern China, demonstrating the framework’s effectiveness and broad applicability in complex systems.

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: Representation, reconstruction, and subgraph or motif discovery in 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

Abstract

This study proposes Structural Gating and Effect-aligned Discovery for Temporal Causal Discovery (SGED-TCD), a novel and general framework for lag-resolved causal discovery in complex multivariate time series. SGED-TCD combines explicit structural gating, stability-oriented learning, perturbation-effect alignment, and unified graph extraction to improve the interpretability, robustness, and functional consistency of inferred causal graphs. To evaluate its effectiveness in a representative real-world setting, we apply SGED-TCD to teleconnection-driven compound heatwave--air-pollution extremes in eastern and northern China. Using large-scale climate indices, regional circulation and boundary-layer variables, and compound extreme indicators, the framework reconstructs weighted causal networks with explicit dominant lags and relative causal importance. The inferred networks reveal clear regional and seasonal heterogeneity: warm-season extremes in Eastern China are mainly linked to low-latitude oceanic variability through circulation, radiation, and ventilation pathways, whereas cold-season extremes in Northern China are more strongly governed by high-latitude circulation variability associated with boundary-layer suppression and persistent stagnation. These results show that SGED-TCD can recover physically interpretable, hierarchical, and lag-resolved causal pathways in a challenging climate--environment system. More broadly, the proposed framework is not restricted to the present application and provides a general basis for temporal causal discovery in other complex domains.

Problem

• Temporal Causal Discovery
• Lag-resolved Causality
• Compound Extremes
• Climate-Environment System
• Multivariate Time Series

Innovation

• Structural Gating
• Effect-aligned Learning
• Lag-resolved Causal Discovery
• Temporal Causal Graph
• Stability-oriented Learning