Counterfactual fairness is a framework for modeling fairness using causal inference. It defines fairness as a decision being the same in the actual world and a counterfactual world where an individual belongs to a different demographic group. The paper introduces this concept and demonstrates its application in predicting law school success fairly. The authors argue that traditional fairness definitions may not always address discrimination effectively, and that causal modeling provides a more robust approach. They propose an algorithm that enforces counterfactual fairness by ensuring that predictions remain unchanged when an individual's protected attributes are altered. The framework is tested on real-world data, showing that it can mitigate biases in prediction tasks. The paper also discusses the implications of counterfactual fairness, including its relationship to other fairness concepts and the challenges of implementing it in practice. The authors emphasize the importance of explicitly modeling causal relationships to ensure fair outcomes in machine learning applications.Counterfactual fairness is a framework for modeling fairness using causal inference. It defines fairness as a decision being the same in the actual world and a counterfactual world where an individual belongs to a different demographic group. The paper introduces this concept and demonstrates its application in predicting law school success fairly. The authors argue that traditional fairness definitions may not always address discrimination effectively, and that causal modeling provides a more robust approach. They propose an algorithm that enforces counterfactual fairness by ensuring that predictions remain unchanged when an individual's protected attributes are altered. The framework is tested on real-world data, showing that it can mitigate biases in prediction tasks. The paper also discusses the implications of counterfactual fairness, including its relationship to other fairness concepts and the challenges of implementing it in practice. The authors emphasize the importance of explicitly modeling causal relationships to ensure fair outcomes in machine learning applications.