Adaptive Event-Driven Labeling: A Neuro-Symbolic Multiagent Framework for Causal Inference in Non-Stationary Time Series

Abstract

Identifiability in structural causal models remains a persistent challenge in high dimensional nonstationary environments where latent shocks are obscured by information asymmetries and regime shifts. Traditional econometric methods often rely on rigid recursive assumptions or sign restrictions that falter during periods of extreme volatility. This paper proposes Adaptive Event Driven Labeling (AEDL), a novel neurosymbolic framework that synthesizes unstructured semantic data with formal causal inference to disentangle simultaneous supply and demand shocks. We introduce a Heterogeneous Multiagent Discussion (HAD) architecture, wherein Large Language Model agents with distinct analytical personas engage in dialectic consensus protocols to generate continuous intensity weighted narrative instruments. To mitigate hallucination and enforce economic coherence, the framework integrates a symbolic verification layer and a Reflexion mechanism that iteratively updates causal priors based on posterior market deviations. Empirical validation on global energy market data from 2020 to 2025 demonstrates that the proposed framework significantly outperforms standard set identification techniques, achieving a supply shock identification F1 score of 0.89. Specifically, the model successfully decomposed the February 2021 Texas Freeze by identifying a supply shock magnitude of 0.8 and a demand shock magnitude of 0.7, which attributed 60.0% of the immediate price spike to supply constraints. Furthermore, the Reflexion mechanism corrected initial physical supply estimates for the 2022 Ukraine invasion from 0.9 down to 0.4, while accurately isolating a geopolitical uncertainty premium of 0.95. By rigorously bridging natural language reasoning with time series econometrics, this work advances the state of the art in automated causal discovery for macro scale complex systems.