  ProPublica's analysis of recidivism predictions produced by Correctional
Offender Management Profiling for Alternative Sanctions (COMPAS) software tool
for the task, has shown that the predictions were racially biased against
African American defendants. We analyze the COMPAS data using a causal
reformulation of the underlying algorithmic fairness problem. Specifically, we
assess whether COMPAS exhibits racial bias against African American defendants
using FACT, a recently introduced causality grounded measure of algorithmic
fairness. We use the Neyman-Rubin potential outcomes framework for causal
inference from observational data to estimate FACT from COMPAS data. Our
analysis offers strong evidence that COMPAS exhibits racial bias against
African American defendants. We further show that the FACT estimates from
COMPAS data are robust in the presence of unmeasured confounding.
