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Decision-focused learning (DFL) is an emerging paradigm in machine learningwhich trains a model to optimize decisions, integrating prediction andoptimization in an end-to-end system. This paradigm holds the promise torevolutionize decision-making in many real-world applications which operateunder uncertainty, where the estimation of unknown parameters within thesedecision models often becomes a substantial roadblock. This paper presents acomprehensive review of DFL. It provides an in-depth analysis of the varioustechniques devised to integrate machine learning and optimization models,introduces a taxonomy of DFL methods distinguished by their uniquecharacteristics, and conducts an extensive empirical evaluation of thesemethods proposing suitable benchmark dataset and tasks for DFL. Finally, thestudy provides valuable insights into current and potential future avenues inDFL research.

Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities