用可解释因果模型分析能源市场动态变化，支持真实场景下的反事实推演。
Causal Regime Detection in Energy Markets With Augmented Time Series Structural Causal Models
- 基于神经因果发现构建时变因果图，无需先验图结构
- 实现对风电等可再生能源不同情景下的电价反事实推演
- 适用于电力市场研究者与政策制定者进行机制分析
能源市场中天气、发电技术与价格形成之间存在复杂的因果关系，且制度变迁持续发生而非离散突变。现有方法缺乏显式的因果解释能力或反事实推理功能。本文提出增强型时间序列因果模型（ATSCM），将反事实推理框架拓展至多变量时序数据，通过学习因果结构实现对能源系统的可解释建模，涵盖天气、发电组合、需求模式及可观测市场变量。利用神经因果发现算法，无需依赖真实图结构即可学习时变因果图。在真实电力价格数据上的应用表明，该模型能支持诸如“在不同可再生能源发电情景下电价会如何变化”等新型反事实分析。
原文摘要 · Abstract (English)
Energy markets exhibit complex causal relationships between weather patterns, generation technologies, and price formation, with regime changes occurring continuously rather than at discrete break points. Current approaches model electricity prices without explicit causal interpretation or counterfactual reasoning capabilities. We introduce Augmented Time Series Causal Models (ATSCM) for energy markets, extending counterfactual reasoning frameworks to multivariate temporal data with learned causal structure. Our approach models energy systems through interpretable factors (weather, generation mix, demand patterns), rich grid dynamics, and observable market variables. We integrate neural causal discovery to learn time-varying causal graphs without requiring ground truth DAGs. Applied to real-world electricity price data, ATSCM enables novel counterfactual queries such as "What would prices be under different renewable generation scenarios?".
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