  There is currently a dearth of appropriate methods to estimate the causal
effects of multiple treatments when the outcome is binary. For such settings,
we propose the use of nonparametric Bayesian modeling, Bayesian Additive
Regression Trees (BART). We conduct an extensive simulation study to compare
BART to several existing, propensity score-based methods and to identify its
operating characteristics when estimating average treatment effects on the
treated. BART consistently demonstrates low bias and mean-squared errors. We
illustrate the use of BART through a comparative effectiveness analysis of a
large dataset, drawn from the latest SEER-Medicare linkage, on patients who
were operated via robotic-assisted surgery, video-assisted thoratic surgery or
open thoracotomy.
