RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation

ArXi:2605.05890v1 Announce Type: new Estimating causal effects from observational data has become increasingly critical in diverse fields including healthcare, economics, and social policy. The fundamental challenge in causal inference arises from the missing counterfactuals and the selection bias. Existing methods are largely limited to point estimates and lack the capacity for distribution modeling.