使用新型机器学习方法分析电力市场自由化对电价的因果影响
Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods
AI总结：
本文研究美国电力市场自由化对电价的因果影响，引入Causal Machine Learning方法，发现DeepProbCP框架表现最优，且自由化使短期电价下降7%。
摘要：
能源与金融市场之间的关系日益重要。随着世界面临满足人类日益增长的能源需求及应对气候变化影响等挑战，理解这些关系对政策制定者和其他利益相关者至关重要。本文研究了美国电力市场自由化对电价的因果影响。通过进行此分析，我们旨在为关于电力市场自由化益处的持续辩论提供新见解。我们引入 Causal Machine Learning（因果机器学习）作为能源金融领域干预的新方法。近年来机器学习的发展为用于因果影响分析的新机器学习模型分支打开了大门，具备从数据中提取复杂模式和关系的能力。我们讨论了因果 ML 方法的优势，并比较了基于 ML 的模型性能，以阐明因果 ML 框架在能源政策干预案例中的适用性。我们发现 DeepProbCP 框架优于所考察的其他框架。此外，我们发现电力市场的自由化及个体参与者的进入导致短期内价格下降了7%。
英文摘要：
Relationships between the energy and the finance markets are increasingly important. Understanding these relationships is vital for policymakers and other stakeholders as the world faces challenges such as satisfying humanity's increasing need for energy and the effects of climate change. In this paper, we investigate the causal effect of electricity market liberalization on the electricity price in the US. By performing this analysis, we aim to provide new insights into the ongoing debate about the benefits of electricity market liberalization. We introduce Causal Machine Learning as a new approach for interventions in the energy-finance field. The development of machine learning in recent years opened the door for a new branch of machine learning models for causality impact, with the ability to extract complex patterns and relationships from the data. We discuss the advantages of causal ML methods and compare the performance of ML-based models to shed light on the applicability of causal ML frameworks to energy policy intervention cases. We find that the DeepProbCP framework outperforms the other frameworks examined. In addition, we find that liberalization of, and individual players' entry to, the electricity market resulted in a 7% decrease in price in the short term.
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