Unfairness Detection within Power Systems through Transfer Counterfactual Learning

Song Wei, Xiangrui Kong, Sarah Ann Huestis-Mitchell, Yao Xie, Shixiang Zhu, Alinson Santos Xavier, Feng Qiu

Keywords: Causal inference; Energy justice; Transfer Learning

TL;DR: This work presents a homogeneous transfer learning approach within the inverse probability weighting estimation to tackle the data sacristy and improve estimation accuracy of the causal effect.

Abstract:

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.

Submission Number: 29