Contextual Optimization Under Model Misspecification: A Tractable and Generalizable Approach

Contextual optimization problems are prevalent in decision-making applications where historical data and contextual features are used to learn predictive models that inform optimal actions. However, practical applications often suffer from model misspecification due to incomplete knowledge of the underlying data-generating process, leading to suboptimal decisions. Existing approaches primarily address the well-specified case, leaving a critical gap in handling misspecified models. In this paper, we propose a novel Integrated Learning and Optimization (ILO) framework that explicitly accounts for model misspecification by introducing a tractable surrogate loss function with strong theoretical guarantees on generalizability, tractability, and optimality. Our surrogate loss aligns with the true decision performance objective, ensuring robustness to misspecification without imposing restrictive assumptions. The proposed approach effectively mitigates the challenges of non-convexity and non-smoothness in the target loss function, leading to efficient optimization procedures. We provide rigorous theoretical analysis and experimental validation, demonstrating superior performance compared to state-of-the-art methods. Our work offers a principled solution to the practically relevant challenge of model misspecification in contextual optimization.

We study the setting where decisions are made based on predictions given by a machine learning model. We develop a new algorithm that trains machine learning models to take into account the quality of the decision, rather than the accuracy of the prediction. When the model at hand is unable to make perfect predictions, it is unclear whether state of the art approaches are able to make predictions that yield optimal decisions. Our new approach mitigates this gap in the literature and can provably output predictions that yield the best possible decisions.