CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness

ArXi:2509.15199v2 Announce Type: replace Causal fairness in databases is crucial to preventing biased and inaccurate outcomes in downstream tasks. While most prior work assumes a known causal model, recent efforts relax this assumption by enforcing additional constraints. However, these approaches often fail to capture broader attribute relationships that are critical to maintaining utility.