Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context

AbstractResponsive survey designs rely upon incoming data from the field data collection to optimize cost and quality tradeoffs. In order to make these decisions in real-time, survey managers rely upon monitoring tools that generate proxy indicators for cost and quality. There is a developing literature on proxy indicators for the risk of nonresponse bias. However, there is very little research on proxy indicators for costs and almost none aimed at predicting costs under alternative design strategies. Predictions of survey costs and proxy error indicators can be used to optimize survey designs in real time. Using data from the National Survey of Family Growth, we evaluate alternative modeling strategies aimed at predicting survey costs (specifically, interviewer hours). The models include multilevel regression (with random interviewer effects) and Bayesian Additive Regression Trees (BART).

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BARP: Improving Mister P Using Bayesian Additive Regression Trees

American Political Science Review ◽

10.1017/s0003055419000480 ◽

2019 ◽

Vol 113 (4) ◽

pp. 1060-1065 ◽

Cited By ~ 1

Author(s):

JAMES BISBEE

Keyword(s):

Multilevel Model ◽

Regression Trees ◽

R Package ◽

Substantial Improvement ◽

Regularization Methods ◽

Multilevel Regression ◽

Nonparametric Approach ◽

Additive Regression ◽

Post Stratification ◽

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.

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Incorporating external data into the analysis of clinical trials via Bayesian additive regression trees

Statistics in Medicine ◽

10.1002/sim.9191 ◽

2021 ◽

Author(s):

Tianjian Zhou ◽

Yuan Ji

Keyword(s):

Clinical Trials ◽

Regression Trees ◽

External Data ◽

Additive Regression ◽

Bayesian Additive Regression Trees

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BART: Bayesian additive regression trees

The Annals of Applied Statistics ◽

10.1214/09-aoas285 ◽

2010 ◽

Vol 4 (1) ◽

pp. 266-298 ◽

Cited By ~ 440

Author(s):

Hugh A. Chipman ◽

Edward I. George ◽

Robert E. McCulloch

Keyword(s):

Regression Trees ◽

Additive Regression ◽

Bayesian Additive Regression Trees

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Variable Selection and Interaction Detection with Bayesian Additive Regression Trees

10.1201/9781003089018-17 ◽

2021 ◽

pp. 395-414

Author(s):

Carlos M. Carvalho ◽

Edward I. George ◽

P. Richard Hahn ◽

Robert E. McCulloch

Keyword(s):

Variable Selection ◽

Regression Trees ◽

Interaction Detection ◽

Additive Regression ◽

Bayesian Additive Regression Trees

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Estimation of causal effects of multiple treatments in observational studies with a binary outcome

Statistical Methods in Medical Research ◽

10.1177/0962280220921909 ◽

2020 ◽

Vol 29 (11) ◽

pp. 3218-3234 ◽

Cited By ~ 7

Author(s):

Liangyuan Hu ◽

Chenyang Gu ◽

Michael Lopez ◽

Jiayi Ji ◽

Juan Wisnivesky

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimator ◽

Regression Trees ◽

Likelihood Estimator ◽

Inverse Probability ◽

Common Support ◽

Targeted Maximum Likelihood ◽

Additive Regression ◽

Multiple Treatments ◽

Bayesian Additive Regression Trees

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.

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