  Recent advances in the literature have demonstrated that standard supervised
learning algorithms are ill-suited for problems with endogenous explanatory
variables. To correct for the endogeneity bias, many variants of nonparameteric
instrumental variable regression methods have been developed. In this paper, we
propose an alternative algorithm called boostIV that builds on the traditional
gradient boosting algorithm and corrects for the endogeneity bias. The
algorithm is very intuitive and resembles an iterative version of the standard
2SLS estimator. Moreover, our approach is data driven, meaning that the
researcher does not have to make a stance on neither the form of the target
function approximation nor the choice of instruments. We demonstrate that our
estimator is consistent under mild conditions. We carry out extensive Monte
Carlo simulations to demonstrate the finite sample performance of our algorithm
compared to other recently developed methods. We show that boostIV is at worst
on par with the existing methods and on average significantly outperforms them.
