Moving towards a more equal world, one ride at a time: Studying Public Transportation Initiatives using interpretable causal inference

Abstract

The goal of low-income fare subsidy programs is to increase equitable access to public transit, and in doing so, increase access to jobs, housing, education and other essential resources. King County Metro, one of the largest transit providers focused on equitable public transit, has been innovative in launching new programs for low-income riders. However, due to the observational nature of data on ridership behavior in King County, evaluating the effectiveness of such innovative policies is difficult. In this work, we use a recent interpretable machine-learning-based causal inference matching method called FLAME to evaluate one of King County Metro’s largest programs implemented in 2020 - the Subsidized Annual Pass.