Flexible instrumental variable models with Bayesian additive regression trees

Abstract

Methods utilizing instrumental variables have been a fundamental statistical approach to causal estimation in the presence of unmeasured confounding, usually occurring in nonrandomized observational data common to fields such as economics and public health. However, such methods traditionally make constricting linearity and additivity assumptions that are inapplicable to the complex modeling challenges of today. The growing body of observational data being collected may benefit from flexible regression modeling while also retaining the ability to control for confounding using instrumental variables. Therefore, this article presents a flexible instrumental variable regression model based on Bayesian regression tree ensembles to estimate the causal exposure-outcome relationship, including interactions with covariates, in the presence of confounding. One exciting application of this method is to use genetic variants as instruments, known as Mendelian randomization. We present our flexible Bayesian instrumental variable regression tree method with an example from the UK Biobank where body mass index is related to blood pressure using genetic variants as the instruments. Body mass index is one factor that is hypothesized to have a nonlinear relationship with cardiovascular risk factors, such as blood pressure, while interacting with age. Heterogeneity in patient characteristics, such as age, could be clinically interesting from a precision medicine perspective where individualized treatment is emphasized.