  Post-stratification is often used to estimate treatment effects with higher
efficiency. However, the majority of existing post-stratification frameworks
depend on prior knowledge of the distributions of covariates and assume that
the units are classified into post-strata without error. We propose a novel
method to determine a proper stratification rule by mapping the covariates into
a post-stratification factor (PSF) using predictive regression models. Inspired
by the bootstrap aggregating (bagging) method, we utilize the out-of-bag
delete-D jackknife to estimate strata boundaries, strata weights, and the
variance of the point estimate. Confidence intervals are constructed with these
estimators to take into account the additional variability coming from
uncertainty in the strata boundaries and weights. Extensive simulations show
that our proposed method consistently improves the efficiency of the estimates
when the regression models are predictive and tends to be more robust than the
regression imputation method.
