Bayesian additive regression trees for causal inference with multiple treatments and a binary outcome

In this project, we propose a new approach that leverages Bayesian machine learning techniques for estimating treatment effect of multiple treatments. By conducting extensive simulation analyses, we examine the operating characteristics of our proposed approach under a variety of contextually motivated settings and compare its performance against existing methods. We further develop a novel and interpretable Bayesian framework to evaluate the sensitivity of our method to unmeasured variables. Finally, we apply proposed methods for estimating causal effects and handling the underlying assumptions to a Surveillance, Epidemiology, and End Results- Medicare (SEER-Medicare) data for lung cancer to compare the effectiveness of multiple surgical management approaches, including novel approaches using robotic systems, on perioperative mortality and occurrence of adverse events—an important and emerging cancer research question.