The causal impact of bail on case outcomes for indigent defendants

TL;DR

This paper investigates whether bail causally increases the likelihood of conviction for indigent defendants using a causal inference technique, providing evidence of a significant impact.

Contribution

It applies near-far matching to establish a causal link between bail and case outcomes, a novel approach in this context.

Findings

Bail causally increases conviction likelihood

Strong evidence of causal impact

Method demonstrates effectiveness in legal outcome analysis

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.