  We explore how much knowing a parametric restriction on propensity scores
improves semiparametric efficiency bounds in the potential outcome framework.
For stratified propensity scores, considered as a parametric model, we derive
explicit formulas for the efficiency gain from knowing how the covariate space
is split. Based on these, we find that the efficiency gain decreases as the
partition of the stratification becomes finer. For general parametric models,
where it is hard to obtain explicit representations of efficiency bounds, we
propose a novel framework that enables us to see whether knowing a parametric
model is valuable in terms of efficiency even when it is high-dimensional. In
addition to the intuitive fact that knowing the parametric model does not help
much if it is sufficiently flexible, we discover that the efficiency gain can
be nearly zero even though the parametric assumption significantly restricts
the space of possible propensity scores.
