Semi-Structured Deep Distributional Regression: Combining Structured Additive Models and Deep Learning

Abstract

Combining additive models and neural networks allows to broaden the scope of statistical regression and extends deep learning-based approaches by interpretable structured additive predictors at the same time. Existing approaches uniting the two modeling approaches are, however, limited to very specific combinations and, more importantly, involve an identifiability issue. As a consequence, interpretability and stable estimation is typically lost. We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. To overcome the inherent identifiability issues between different model parts, we construct an orthogonalization cell that projects the deep neural network into the orthogonal complement of the statistical model predictor. This enables proper estimation of structured model parts and thereby interpretability. We demonstrate the framework's efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications