  In this paper, we introduce a framework for solving finite-horizon multistage
optimization problems under uncertainty in the presence of auxiliary data. We
assume the joint distribution of the uncertain quantities is unknown, but noisy
observations, along with observations of auxiliary covariates, are available.
We utilize effective predictive methods from machine learning (ML), including
$k$-nearest neighbors regression ($k$NN), classification and regression trees
(CART), and random forests (RF), to develop specific methods that are
applicable to a wide variety of problems. We demonstrate that our solution
methods are asymptotically optimal under mild conditions. Additionally, we
establish finite sample guarantees for the optimality of our method with $k$NN
weight functions. Finally, we demonstrate the practicality of our approach with
computational examples. We see a significant decrease in cost by taking into
account the auxiliary data in the multistage setting.
