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What is a relevant control?: An algorithmic proposal

Author

Listed:
  • Fernando Delbianco

    (UNS/CONICET)

  • Fernando Tohmé

    (UNS/CONICET)

Abstract

Individualized inference (or prediction) is an approach to data analysis that is increasingly relevant thanks to the availability of large datasets. In this paper, we present an algorithm that starts by detecting the relevant observations for a given query. Further refinement of that subsample is obtained by selecting the ones with the largest Shapley values. The probability distribution over this selection allows to generate 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

  • Fernando Delbianco & Fernando Tohmé, 2023. "What is a relevant control?: An algorithmic proposal," Working Papers 269, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:269
    as

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    File URL: https://rednie.eco.unc.edu.ar/files/DT/269.pdf
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    References listed on IDEAS

    as
    1. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
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    3. Xinran Li & Xiao-Li Meng, 2021. "A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 353-367, January.
    4. Salvador Romaguera, 2022. "Basic Contractions of Suzuki-Type on Quasi-Metric Spaces and Fixed Point Results," Mathematics, MDPI, vol. 10(21), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Individualized inference; Relevance selection; and classification; Synthetic controls;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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