Explainable Performance

Sullivan Hué, Christophe Hurlin, Christophe Pérignon and Sébastien Saurin
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Christophe Pérignon: HEC Paris - Ecole des Hautes Etudes Commerciales
Sébastien Saurin: UO - Université d'Orléans

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Abstract: We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features, XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.

Keywords: Machine learning; Explainability; Performance metric; Shapley value (search for similar items in EconPapers)
Date: 2022-12-12
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Working Paper: Explainable Performance (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-03897380

DOI: 10.2139/ssrn.4280563

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