Keywords: causal inference, multiple treatments, BART, matching
Approaches for estimating the causal effects of a binary treatment have been well documented. Causal effect estimation with multiple treatments, meanwhile, requires additional assumptions and more refined techniques. Disappointingly, these methods have generally been inaccessible to practitioners, many of which have resorted to dichotomization of the treatments assignment mechanism in order to apply more familiar tools. We demonstrate the usefulness of Bayesian Additive Regression Trees (BART) for causal inference with multiple treatments. Among its advantages, BART is straightforward to implement, requires fewer user inputs, and operates smoothly with large numbers of covariates. We explore the operating characteristics of BART with multiple treatments, and use simulations to compare BART to alternative approaches, including matching on vectors of generalized propensity scores and inverse probability weighting. We conclude by using each approach to estimate the causal effects of radical prostatectomy versus two radiotherapy modalities on the five-year survival rate among high-risk localized prostate cancer patients.