Causal Fairness

Causal fairness in machine learning aims to develop algorithms that make unbiased decisions by explicitly modeling and mitigating the causal relationships between sensitive attributes (e.g., race, gender), predictor variables, and the outcome. Current research focuses on developing methods that leverage causal graphs and interventional approaches to identify and remove discriminatory causal pathways, often employing neural networks or constrained optimization techniques for fair prediction. This field is crucial for ensuring fairness and equity in AI systems across various applications, from loan applications to healthcare, by moving beyond simple statistical correlations to address the root causes of bias.