As machine learning (ML) systems are increasingly deployed in high-stakes domains, the need for robust methods to assess fairness has become more critical. While statistical fairness metrics are widely used due to their simplicity, they are limited in their ability to explain why disparities occur, as they rely on associative relationships in the data. In contrast, causal fairness metrics aim to uncover the underlying data-generating mechanisms that lead to observed disparities, enabling a deeper understanding of the influence of sensitive attributes and their proxies. Despite their promise, causal fairness metrics have seen limited adoption due to their technical and computational complexity. To address this gap, we present CausalFairness, the first open-source Python package designed to compute a diverse set of causal fairness metrics at both the group and individual levels. The metrics implemented are broadly applicable across classification and regression tasks (with easy extensions for intersectional analysis) and were selected for their significance in the fairness literature. We also demonstrate how standard statistical fairness metrics can be decomposed into their causal components, providing a complementary view of fairness grounded in causal reasoning. In this active learning talk participants will learn how to quantify bias using CausalFairness at the group (Counterfactual Equalized Odds , Counterfactual Effects) and individual (Counterfactual Fairness) levels by applying each method to three datasets - 1) the Adult Income dataset, 2) the COMPAS dataset, 3) Law School Admission Council (LSAC) Dataset. The session will elucidate on the intuition for computing and interpreting each metric, and conclude with a discussion of their limitations.