Multivariate Assessment of a Repair Program for a New York City Electrical Grid

Abstract

We assess the impact of an inspection repair program administered to the secondary electrical grid in New York City. The question of interest is whether repairs reduce the incidence of future events that cause service disruptions ranging from minor to serious ones. A key challenge in defining treatment and control groups in the absence of a randomized experiment involved an inherent bias in selection of electrical structures to be inspected in a given year. To compensate for the bias, we construct separate models for each year of the propensity for a structure to have an inspection repair. The propensity models account for differences across years in the structures that get inspected. To model the treatment outcome, we use a statistical approach based on the additive effects of many weak learners. Our results indicate that inspection repairs are more beneficial earlier in the five-year inspection cycle, which accords with the inherent bias to inspect structures in earlier years that are known to have problems.