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Super-relevant synthetic data with individualized Shapley values

Author

Listed:
  • Delbianco Fernando
  • Tohmé Fernando

Abstract

Individualized inference (or prediction) is an approach to data analysis that provides tailored analytical insights for specific queries. It is increasingly relevant thanks to the availability of large datasets. This paper presents an algorithm that identifies relevant observations through similarity metrics and further refines this selection by weighting with Shapley values. The probability distribution over this selection allows for generating synthetic controls, which in turn can be used to generate a robust inference (or prediction). Data collected from repeating this procedure for different queries provides a deeper understanding of the general process that generates the data.

Suggested Citation

  • Delbianco Fernando & Tohmé Fernando, 2025. "Super-relevant synthetic data with individualized Shapley values," Asociación Argentina de Economía Política: Working Papers 4793, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4793
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    File URL: https://aaep.org.ar/works/works2025/4793.pdf
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    References listed on IDEAS

    as
    1. Fernando Delbianco & Fernando Tohmé, 2025. "Identifying Highly Relevant Entries in Datasets: A Relevance-Based Classification," Journal of Classification, Springer;The Classification Society, vol. 42(3), pages 674-694, November.
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      JEL classification:

      • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
      • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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