The causal impact of bail on case outcomes for indigent defendants in New York City

Abstract

We use near-far matching, a technique for estimating causal relationships, to explore whether bail causes a higher likelihood of conviction. We find evidence of a strong causal impact. This paper was compiled as a submission to the 2017 Fairness, Accountability, and Transparency in Machine Learning (FAT ML) workshop.