Causal Regime Detection in Energy Markets With Augmented Time Series Structural Causal Models

ArXiv ID: 2511.04361 “View on arXiv”

Authors: Dennis Thumm

Abstract

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?”.

Keywords: Neural Causal Discovery, Counterfactual Reasoning, Time-Varying Causal Graphs, Multivariate Time Series, Energy Systems Modeling, Energy Markets (Electricity)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced causal inference theory, neural architectures, and time-varying DAG learning with complex mathematical formalisms, but lacks detailed empirical validation, backtesting results, or specific datasets in the provided excerpt, focusing more on theoretical framework.

  flowchart TD
    A["Research Goal:<br/>Causal Regime Detection in Energy Markets"] --> B["Data: Multivariate Time Series<br/>(Weather, Generation, Price, Demand)"]
    
    B --> C["Methodology:<br/>Augmented Time Series Causal Models<br/>+ Neural Causal Discovery"]
    
    C --> D["Process: Learn Time-Varying<br/>Causal Graphs & Regime Changes"]
    
    D --> E["Process: Counterfactual Reasoning<br/>(e.g., 'Prices under different<br/>renewable generation?')"]
    
    E --> F["Outcome: Interpretable<br/>Energy System Model"]
    
    F --> G["Outcomes:<br/>Causal Regime Detection<br/>+ Counterfactual Queries<br/>(Energy Markets)"]