Energy justice is a growing area of interest in interdisciplinary energy research. However, identifying systematic biases in the energy sector remains challenging due to confounding variables, intricate heterogeneity in treatment effects, and limited data availability. To address these challenges, we introduce a novel approach for counterfactual causal analysis centered on energy justice. We use subgroup analysis to manage diverse factors and leverage the idea of transfer learning to mitigate data scarcity in each subgroup. In our numerical analysis, we apply our method to a large-scale customer-level power outage data set and investigate the counterfactual effect of demographic factors, such as income and age of the population, on power outage durations. Our results indicate that low-income and elderly-populated areas consistently experience longer power outages, regardless of weather conditions. This points to existing biases in the power system and highlights the need for focused improvements in areas with economic challenges.

翻译：能源正义是跨学科能源研究日益关注的热点领域。然而，由于混杂变量、处理效应的复杂异质性以及数据可用性有限，识别能源系统中的系统性偏差仍然具有挑战性。为解决这些问题，我们提出了一种以能源正义为核心的反事实因果分析新方法。我们采用子组分析来管理多元因素，并利用迁移学习的思想缓解每个子组的数据稀缺问题。在数值分析中，我们将该方法应用于大规模用户级停电数据集，探讨人口因素（如收入和年龄）对停电持续时间的反事实影响。结果表明，无论天气条件如何，低收入和老年人口集中区域的停电时间始终更长。这揭示了电力系统中存在的现有偏差，并凸显了在经济困难地区进行针对性改进的必要性。