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In healthcare, making the best possible predictions with complex models(e.g., neural networks, ensembles/stacks of different models) can impactpatient welfare. In order to make these complex models explainable, we presentDeepSHAP for mixed model types, a framework for layer wise propagation ofShapley values that builds upon DeepLIFT (an existing approach for explainingneural networks). We show that in addition to being able to explain neuralnetworks, this new framework naturally enables attributions for stacks of mixedmodels (e.g., neural network feature extractor into a tree model) as well asattributions of the loss. Finally, we theoretically justify a method forobtaining attributions with respect to a background distribution (under aShapley value framework).

Explaining Models by Propagating Shapley Values of Local Components