A Semiparametric Approach to Interpretable Machine Learning
This addresses the need for transparent models in critical decision-making, offering a method to enhance interpretability without sacrificing performance, though it is incremental by building on existing semiparametric and dimension reduction techniques.
The paper tackles the trade-off between interpretability and predictive performance in machine learning by proposing a semiparametric approach that combines an interpretable parametric model with a nonparametric residual component, achieving improved accuracy in simulations and a real-world ICU length-of-stay prediction application.
Black box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical decision-making processes. In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods. We achieve this by utilizing a two-piece model: the first piece is interpretable and parametric, to which a second, uninterpretable residual piece is added. The performance of the overall model is optimized using methods from the sufficient dimension reduction literature. Influence function based estimators are derived and shown to be doubly robust. This allows for use of approaches such as double Machine Learning in estimating our model parameters. We illustrate the utility of our approach via simulation studies and a data application based on predicting the length of stay in the intensive care unit among surgery patients.