The next Workshop on Quantitative Methods in Education, Health, and the Social Sciences (QMeHSS) will take place on Friday, April 1st, from 11:00am - 12:30pm in NORC conference room 344. Dr. Jennifer Hill from New York University will be joining us to lead our workshop, so please join us in giving her a warm welcome. NORC is located at 1155 E. 60th Street. We look forward to seeing you there.
Flexible, interpretable framework for assessing sensitivity to unmeasured confounding
Professor of Applied Statistics and Data Science, New York University
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 a Bayesian nonparametric fitting algorithm 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.