title: A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.
abstract: When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
authors.affiliation: Humanities & the Social Sciences, New York University, New York, NY, U.S.A.
authors.forename: Vincent
authors.initials: V
authors.lastname: Dorie
authors.affiliation: Economics, Fukuoka University, Fukuoka, Japan.
authors.forename: Masataka
authors.initials: M
authors.lastname: Harada
authors.affiliation: Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, U.S.A.
authors.forename: Nicole Bohme
authors.identifier: 0000-0001-7664-6682
authors.initials: NB
authors.lastname: Carnegie
authors.affiliation: Humanities & the Social Sciences, New York University, New York, NY, U.S.A.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hill
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D009749:Nutrition Surveys
keywords: Bayesian modeling; causal inference; nonparametric regression; sensitivity analysis; unmeasured confounding