  We provide a new flexible framework for inference with the instrumental
variable model. Rather than using linear specifications, functions
characterizing the effects of instruments and other explanatory variables are
estimated using machine learning via Bayesian Additive Regression Trees (BART).
Error terms and their distribution are inferred using Dirichlet Process
mixtures. Simulated and real examples show that when the true functions are
linear, little is lost. But when nonlinearities are present, dramatic
improvements are obtained with virtually no manual tuning.
