Hungary For More? Optimal 1-to-Many Matching for Causal Inference
The Hungarian algorithm (Kuhn–Munkres) efficiently finds optimal one-to-one matches between treated and control units by minimizing total matching cost (typically Euclidean distance in covariate space). It has been used for estimating treatment effects via matching.
But it has a limitation: it is strictly one-to-one.
In many causal inference settings,