This standard describes how to perform causal fairness analysis to make fairer decisions in various high-stakes applications (e. g. credit, employment, education) that are more likely to be compliant with a country's antidiscrimination laws and regulations. It provides a standardized fairness model that encodes knowledge and assumptions about how to map the causal relationships between different variables such as a protected class (e. g. race, gender) and an outcome. The document provides the reader with a standardized language for directly translating concepts among the law, causal inference, and supervised machine learning. The standard provides criteria for selecting which variables to include in a machine learning model, how to train and deploy the model to make fairer predictions and decisions, as well as how to evaluate the model to determine the likelihood for illegal discrimination. However, specific algorithms for debiasing the data in a machine learning model to help ensure fairness are not covered in this standard. Finally, this standard is designed to be focused solely on legal compliance.