  Building machine learning models that are fair with respect to an
unprivileged group is a topical problem. Modern fairness-aware algorithms often
ignore causal effects and enforce fairness through modifications applicable to
only a subset of machine learning models. In this work, we propose a new
definition of fairness that incorporates causality through the Controlled
Direct Effect (CDE). We develop regularizations to tackle classical fairness
measures and present a causal regularization that satisfies our new fairness
definition by removing the impact of unprivileged group variables on the model
outcomes as measured by the CDE. These regularizations are applicable to any
model trained using by iteratively minimizing a loss through differentiation.
We demonstrate our approaches using both gradient boosting and logistic
regression on: a synthetic dataset, the UCI Adult (Census) Dataset, and a
real-world credit-risk dataset. Our results were found to mitigate unfairness
from the predictions with small reductions in model performance.
