Super-relevant synthetic data with individualized Shapley values

Fernando Delbianco and Fernando Tohmé

No 4793, Asociación Argentina de Economía Política: Working Papers from Asociación Argentina de Economía Política

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.

JEL-codes: C4 C6 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2025-12
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Persistent link: https://EconPapers.repec.org/RePEc:aep:anales:4793

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