Decision-Value Attribution in Predict-then-Optimize Systems

2026-06-29

AI Summary

This work addresses the limitation of existing explanation methods, which focus solely on prediction accuracy and fail to capture the actual decision-making value of predictions within downstream optimization systems. To bridge this gap, the authors propose a Decision Value Attribution (DVA) framework that, for the first time, extends Shapley values to joint prediction-optimization pipelines. By formulating a cooperative game whose value function is defined by operational objectives, DVA attributes decision value to input features (InfoDVA), optimization design choices (DesignDVA), and their interactions (DVI). The framework further distinguishes between pre-DVA and post-DVA evaluation paradigms to assess the alignment between model beliefs and realized operational performance. Experiments on electricity storage arbitrage and emergency medical service coverage demonstrate that conventional prediction explanations often misrepresent true decision value, and that optimization configurations critically modulate the decision relevance of predictive information.

Abstract

Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value. We propose Decision Value Attribution (DVA), a Shapley-based framework for attributing the value of a fixed prediction--optimization pipeline. The framework defines cooperative games whose payoff is the downstream decision value, allowing the players to be information sources, optimization or design parameters, or both. We present three variants: InfoDVA attributes value to features, DesignDVA attributes value to operational configurations, and Decision-Value Interactions (DVI) quantifies how information and design jointly create value. We further distinguish post-DVA, which evaluates decisions using realized outcomes, from pre-DVA, which evaluates decisions under the model's full prediction. This separation turns attribution into a decision-level diagnostic of whether the model's operational beliefs align with realized performance. The resulting attributions are expressed in the units of the operational objective and decompose the gain or loss relative to a baseline. Case studies in electricity storage arbitrage and emergency medical service coverage show that predictive explanations can be poor proxies for operational value, that DVA can guide targeted information-control interventions, and that optimization configurations determine when predictive information is decision-relevant.

Problem

• Predict-then-Optimize
• Decision Value Attribution
• Shapley Values
• Operational Decision-Making
• Model Interpretability

Innovation

• Decision Value Attribution
• Predict-then-Optimize
• Shapley Value
• Operational Decision-Making
• Value Decomposition