Bayesian Additive Regression Tree (BART) and Synthetic Random Forest methods are evaluated for the estimation of individual causal effects with nonbinary treatments, and on data with as little simulation as possible. For this purpose, data from a small randomized, cross-over, controlled trial is used. In general, of those methods evaluated, BART performed the best on the dataset.
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