Decision-making with Side Information: A Causal Transport Robust Approach

We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions.¬†In particular, we propose to consider a distributionally robust formulation based on causal transport distance.¬†Compared with divergence and Wasserstein metric, the causal transport distance is better at capturing the information structure revealed from the conditional distribution … Read more

Data-driven Multistage Distributionally Robust Optimization with Nested Distance: Time Consistency and Tractable Dynamic Reformulations

We study multistage distributionally robust optimization in which the uncertainty set consists of stochastic processes that are close to a scenario tree in the nested distance (Pflug and Pichler (2012)). Compared to other choices such as Wasserstein distance between stochastic processes, the nested distance accounts for information evolution, making it hedge against a plausible family … Read more