
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.
