Training outcome models by differentiating a value estimate through dual prices yields allocation policies that respect long-run capacity limits and beat decision-blind predict-then-optimize baselines on deployment-adjusted value across six datasets.
InProceedings of the Second International Conference on Knowledge Discovery and Data Mining, 202–207
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Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints
Training outcome models by differentiating a value estimate through dual prices yields allocation policies that respect long-run capacity limits and beat decision-blind predict-then-optimize baselines on deployment-adjusted value across six datasets.