 Solution

Matching in causal inference can improve the comparison by balancing measured pre-treatment covariates and restricting it to non-UTCs resembling UTCs. Under conditional exchangeability given the matching variables, adequate common support, and consistent treatment definitions, it can estimate an effect for the matched population.

It does not remove bias from unmeasured or poorly measured factors such as prior apprenticeship culture, catchment-area opportunities, selection of motivated pupils, or pre-conversion trends. Matching percentages also need not balance their nonlinear effects or interactions. The proposal is better than the raw comparison, but its causal interpretation still rests on untestable assumptions.