Replication Data for: BARP: Improving Mister P with Bayesian Additive Regression Trees

Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units. However, innovations in non-parametric regularization methods can further improve the researcher's ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more sophisicated methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a non-parametric approach called Bayesian Additive Regression Trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regularization methods to extrapolate opinion data to target geographical units. I provide an R package that implements the BARP method along with other regularization algorithms.