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
counterfactual effects, and limited data availability. First, this paper
demonstrates how one can evaluate counterfactual unfairness in a power system
by analyzing the average causal effect of a specific protected attribute.
Subsequently, we use subgroup analysis to handle model heterogeneity and
introduce a novel method for estimating counterfactual unfairness based on
transfer learning, which helps to alleviate the data scarcity in each subgroup.
In our numerical analysis, we apply our method to a unique 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 under both daily and
post-disaster operations, and such discrimination is exacerbated under severe
conditions. These findings suggest a widespread, systematic issue of injustice
in the power service systems and emphasize the necessity for focused
interventions in disadvantaged communities.Comment: The preliminary version titled "Detecting Electricity Service Equity
Issues with Transfer Counterfactual Learning on Large-Scale Outage Datasets"
is presented at NeurIPS 2023 Workshops on Causal Representation Learning
(CRL) and Algorithmic Fairness through the Lens of Time (AFT); See v