Determining optimal policies in dynamic robust optimization is often challenging due to the curse of dimensionality. As a result, many researchers have proposed restricting attention to classes of simpler decision rules (for instance, static or affine), which are computationally tractable and often deliver excellent performance. In this work, we propose a general theory for analyzing the effectiveness of policies in a large class of dynamic robust optimization problems. Through a transformation of the objective function, we provide a geometric characterization for the effectiveness of a policy class, which is helpful in either establishing the policy’s optimality or bounding its sub-optimality gap. Based on this geometric characterization, we recover and generalize several different results on static and affine polices from the literature. Furthermore, our theory establishes interesting connections with the theory of discrete convexity, global concave envelopes, and the integrality gap of mixed integer linear programs.