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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Dorie V,Harada M,Carnegie NB,Hill J

doi

10.1002/sim.6973

subject

Has Abstract

pub_date

2016-09-10 00:00:00

pages

3453-70

issue

20

eissn

0277-6715

issn

1097-0258

journal_volume

35

pub_type

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