Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fairness have been defined and examined in the past decade, such as statistical parity and equalized odds. The most recent fairness notions, however, are causal-based and reflect the now widely accepted idea that using causality is necessary to appropriately address the problem of fairness. The big impediment to the use of causality to address fairness, however, is the unavailability of the causal model (typically represented as a causal graph). This paper describes a software tool that implements all required steps to estimate discrimination using a causal approach, including, the causal discovery, the adjustment of the causal model, and the estimation of discrimination. The software has a web interface which makes it accessible online without any required setup on the user side.