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A new model for counterfactual analysis for functional data

Author

Listed:
  • Emilio Carrizosa

    (Instituto de Matemáticas de la Universidad de Sevilla)

  • Jasone Ramírez-Ayerbe

    (Instituto de Matemáticas de la Universidad de Sevilla)

  • Dolores Romero Morales

    (Copenhagen Business School)

Abstract

Counterfactual explanations have become a very popular interpretability tool to understand and explain how complex machine learning models make decisions for individual instances. Most of the research on counterfactual explainability focuses on tabular and image data and much less on models dealing with functional data. In this paper, a counterfactual analysis for functional data is addressed, in which the goal is to identify the samples of the dataset from which the counterfactual explanation is made of, as well as how they are combined so that the individual instance and its counterfactual are as close as possible. Our methodology can be used with different distance measures for multivariate functional data and is applicable to any score-based classifier. We illustrate our methodology using two different real-world datasets, one univariate and another multivariate.

Suggested Citation

  • Emilio Carrizosa & Jasone Ramírez-Ayerbe & Dolores Romero Morales, 2024. "A new model for counterfactual analysis for functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 981-1000, December.
    • Handle: RePEc:spr:advdac:v:18:y:2024:i:4:d:10.1007_s11634-023-00563-5
    DOI: 10.1007/s11634-023-00563-5
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    References listed on IDEAS

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