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BARP: Improving Mister P Using Bayesian Additive Regression Trees

Published online by Cambridge University Press:  06 August 2019

JAMES BISBEE Open the ORCID record for JAMES BISBEE [Opens in a new window]
JAMES BISBEE*
Affiliation:
New York University
*
*James Bisbee, PhD Candidate, NYU Wilf Family Department of Politics, New York University, james.bisbee@nyu.edu.
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Abstract

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.

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Type
Letter
Information
American Political Science Review , Volume 113 , Issue 4 , November 2019 , pp. 1060 - 1065
Copyright
Copyright © American Political Science Association 2019 

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Footnotes

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: https://doi.org/10.7910/DVN/LMW871.

References

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Bisbee Dataset

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https://doi.org/10.7910/DVN/LMW871
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