  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.

Machine Learning;Methodology