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A flexible approach for variable selection in the presence of missing data

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sim_mia.R		sim_mia.R
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RR-BART We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique,

Bayesian additive regression trees, and uses Rubin's rule to combine the estimates and variances of the variable importance measures on

multiply imputed datasets for variable selection in the presence of MAR data.

The accompanying paper Lin et al. (2022) "A flexible approach for variable selection in large-scale healthcare database studies with

missing covariate and outcome data" BMC Medical Research Methodology 22, 132.

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A flexible approach for variable selection in the presence of missing data

machine-learning-algorithms variable-selection missing-data

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