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Deep neural networks (DNNs) have proven to be highly effective in a varietyof tasks, making them the go-to method for problems requiring high-levelpredictive power. Despite this success, the inner workings of DNNs are oftennot transparent, making them difficult to interpret or understand. This lack ofinterpretability has led to increased research on inherently interpretableneural networks in recent years. Models such as Neural Additive Models (NAMs)achieve visual interpretability through the combination of classicalstatistical methods with DNNs. However, these approaches only concentrate onmean response predictions, leaving out other properties of the responsedistribution of the underlying data. We propose Neural Additive Models forLocation Scale and Shape (NAMLSS), a modelling framework that combines thepredictive power of classical deep learning models with the inherent advantagesof distributional regression while maintaining the interpretability of additivemodels.

Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean