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.

在能源公正领域，通过针对能源公正进行反事实因果分析，我们发现经济困难地区存在不公正现象，表明电力系统存在现有的偏见，同时也突出了这些领域需要有针对性的改进。

基于大规模停电数据的转移反事实学习技术 识别电力服务公平问题

基于大规模停电数据的转移反事实学习技术识别电力服务公平问题