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?".

翻译：能源市场展现出天气模式、发电技术与价格形成之间复杂的因果关系，其机制变化呈现连续性而非离散断点。现有方法虽能对电价进行建模，但缺乏明确的因果解释与反事实推理能力。本文针对能源市场提出增强时间序列因果模型（ATSCM），将反事实推理框架扩展至具有学习因果结构的多元时序数据。该方法通过可解释因子（天气、发电结构、需求模式）、复杂电网动态及可观测市场变量对能源系统进行建模。我们整合神经因果发现技术以学习时变因果图，无需依赖真实有向无环图。将ATSCM应用于真实电价数据后，该模型能够实现新型反事实查询，例如"在不同可再生能源发电情景下价格将如何变化？"。