  We propose a new perspective for the evaluation of matching procedures by
considering the complexity of the function class they belong to. Under this
perspective we provide theoretical guarantees on post-matching covariate
balance through a finite sample concentration inequality. We apply this
framework to coarsened exact matching as well as matching using the propensity
score and suggest how to apply it to other algorithms. Simulation studies are
used to evaluate the procedures.
