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BARP: Improving Mister P Using 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 nonparametric 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 flexible methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a nonparametric 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.


Corresponding author

*James Bisbee, PhD Candidate, NYU Wilf Family Department of Politics, New York University,


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I am grateful to Neal Beck, Patrick Egan, Shane Mahon, Keith McCart, Kevin Munger, Thiago Moreira da Silva, and Drew Dimmery for their helpful feedback. Replication files are available at the American Political Science Review Dataverse:



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Type Description Title
Supplementary materials

Bisbee Dataset

Supplementary materials

Bisbee supplementary material
Bisbee supplementary material 1

 PDF (960 KB)
960 KB


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