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The vast majority of research on explainability focuses onpost-explainability rather than explainable modeling. Namely, an explanationmodel is derived to explain a complex black box model built with the solepurpose of achieving the highest performance possible. In part, this trendmight be driven by the misconception that there is a trade-off betweenexplainability and accuracy. Furthermore, the consequential work on Shapelyvalues, grounded in game theory, has also contributed to a new wave ofpost-explainability research on better approximations for various machinelearning models, including deep learning models. We propose a new architecturethat inherently produces explainable predictions in the form of additivefeature attributions. Our approach learns a graph representation for eachrecord in the dataset. Attribute centric features are then derived from thegraph and fed into a contribution deep set model to produce the finalpredictions. We show that our explainable model attains the same level ofperformance as black box models. Finally, we provide an augmented modeltraining approach that leverages the missingness property and yields highlevels of consistency (as required for the Shapely values) without loss ofaccuracy.

Explainable Deep Modeling of Tabular Data using TableGraphNet