The COMPAS/ audit in this repo is a direct illustration of counterfactual fairness violation - even without using the causal framework explicitly.
COMPAS assigns recidivism risk scores based on features including prior arrest count, age at first arrest, and custody status. The biased model produces:
-| Group | High-Risk Rate |
|---|---|
| Black defendants | 58.15% |
| White defendants | 28.37% |
| Fairness Gap | 29.78pp |
| Group | High-Risk Flag Rate |
|---|---|
| Black defendants | 87.16% |
| White defendants | 0.40% |
| Fairness Gap | 86.77pp |
Now ask the counterfactual question. Take a Black defendant with 3 prior arrests. Ask: if this person had been white, with the same underlying behaviour and the same 3 prior arrests, would their risk score be the same?
The answer is no - and the reason is CustodyStatus, which the proxy variables explainer identifies as the key proxy. Black communities have been subject to historical over-policing: for the same underlying behaviour, Black individuals are arrested at higher rates than white individuals. Prior arrest count is therefore not causally independent of race - it is partly caused by race, via differential policing. A model trained on prior arrests is therefore using a variable that race, in part, caused.
Race ──→ Policing intensity ──→ Arrest rate ──→ Prior arrests ──→ Risk score