In this talk, we will discuss multistage distributionally robust optimization in which the uncertainty set of stochastic processes is defined through the adapted Wasserstein distance. First, I will present a dynamic programming reformulation to evaluate the worst-case risk of a given stochastic process and discuss the issue of time consistency. Second, in the context of linear and stagewise-independent setting, I will present a class of decision rules, termed "best-neighbor" policy, that are provably robust optimal.