  Generalized Additive Models (GAMs) have recently experienced a resurgence in
popularity due to their interpretability, which arises from expressing the
target value as a sum of non-linear transformations of the features. Despite
the current enthusiasm for GAMs, their susceptibility to concurvity - i.e.,
(possibly non-linear) dependencies between the features - has hitherto been
largely overlooked. Here, we demonstrate how concurvity can severly impair the
interpretability of GAMs and propose a remedy: a conceptually simple, yet
effective regularizer which penalizes pairwise correlations of the non-linearly
transformed feature variables. This procedure is applicable to any
differentiable additive model, such as Neural Additive Models or NeuralProphet,
and enhances interpretability by eliminating ambiguities due to self-canceling
feature contributions. We validate the effectiveness of our regularizer in
experiments on synthetic as well as real-world datasets for time-series and
tabular data. Our experiments show that concurvity in GAMs can be reduced
without significantly compromising prediction quality, improving
interpretability and reducing variance in the feature importances.
