标题： 基于新型机器学习方法的电力市场自由化对电价的影响分析 显示英文标题

标题： Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods

摘要： 能源市场和金融市场之间的关系日益重要。 了解这些关系对政策制定者和其他利益相关者至关重要，因为世界正面临满足人类不断增长的能源需求以及气候变化影响等挑战。 在本文中，我们研究了电力市场自由化对美国电价的因果影响。 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争论提供新的见解。 我们引入了因果机器学习作为一种新的方法，用于能源-金融领域的干预。 近年来机器学习的发展为因果影响的新一类机器学习模型打开了大门，这些模型能够从数据中提取复杂的模式和关系。 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框架优于其他 examined 的框架。 此外，我们发现电力市场的自由化以及个别参与者进入电力市场在短期内导致价格下降了7%。 显示英文摘要

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.