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Workshop: Causal Machine Learning for Real-World Impact

Making the World More Equal, One Ride at a Time: Studying Public Transportation Initiatives Using Interpretable Causal Inference

Gaurav Rajesh Parikh · Albert Sun · Jenny Huang · Lesia Semenova · Cynthia Rudin


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 used seven datasets from a variety of sources, and used 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 (SAP). Using FLAME, we construct high-quality matched groups and identify features that are important for predicting ridership and re-enrollment. Our analysis provides clear and insightful feedback for policy-makers. In particular, we found that SAP is effective in increasing long-term ridership and re-enrollment. Notably, there are pronounced positive treatment effects in populations that have higher access to public transit and jobs. Treatment effects are also more pronounced in the Asian population and in individuals ages 65+. Insights from this work can help broadly inform public transportation policy decisions and generalize broadly to other cities and other forms of transportation.

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