Tree-Based Predictive Models for Noisy Input Data
This work provides a more robust predictive modeling tool for researchers in fields like biomedicine where input data is inherently noisy due to measurement error.
This paper addresses the challenge of measurement error in input data for predictive models, specifically extending Bayesian additive regression trees (BART) to incorporate measurement error (meBART). Through simulations, meBART demonstrated more accurate parameter estimation, more robust uncertainty quantification, and superior predictive performance compared to existing methods.
Measurement error is prevalent across all domains of scientific research where only imprecise observations, rather than the true underlying values, can be obtained. For example, estimates of human microbiome diversity are based on small samples from a much larger, generally unobserved system and reflect both sampling error and technical variation. In high-noise settings like these, it becomes difficult to make accurate predictions and to summarize uncertainty. Methods have previously been proposed to accommodate measurement error in classic predictive models, such as linear regression. However, relatively little work has been done to address measurement error in more complex and flexible models. Bayesian additive regression trees (BART), a Bayesian nonparametric model that sums the output of many decision trees, offers robust predictions with built-in uncertainty quantification. In this work, we propose measurement error BART (meBART), a novel extension to the BART model that directly incorporates measurement error in the independent variable(s). Through simulation studies, we show that in the presence of measurement error, our model enables more accurate parameter estimation, more robust uncertainty quantification, and superior predictive performance. We illustrate the utility of our proposed approach through two biomedical applications where the predictors of interest are subject to measurement error.