An Interpretable Neural Network for Parameter Inference
Johann Pfitzinger
Abstract:Adoption of deep neural networks in fields such as economics or finance has been constrained by the lack of interpretability of model outcomes. This paper proposes a generative neural network architecturethe parameter encoder neural network (PENN) -capable of estimating local posterior distributions for the parameters of a regression model. The parameters fully explain predictions in terms of the inputs and permit visualization, interpretation and inference in the presence of complex heterogeneous effects and … Show more
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