Model uncertainty quantification using feature confidence sets for outcome excursions
It addresses uncertainty quantification for risk management in fields like medicine and finance, offering a novel approach but is incremental in building on traditional interval methods.
The paper tackles the problem of quantifying prediction uncertainty in high-stakes applications by introducing a model-agnostic framework that constructs confidence sets for outcome excursions, identifying feature subsets where outcomes exceed a threshold, with theoretical guarantees validated through simulations and real-world datasets like housing price prediction and sepsis diagnosis.
When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying prediction uncertainty is critical for effective risk management. Traditional approaches to uncertainty quantification, such as confidence and prediction intervals, provide probability coverage guarantees for the expected outcomes $f(\boldsymbol{x})$ or the realized outcomes $f(\boldsymbol{x})+ε$. Instead, this paper introduces a novel, model-agnostic framework for quantifying uncertainty in continuous and binary outcomes using confidence sets for outcome excursions, where the goal is to identify a subset of the feature space where the expected or realized outcome exceeds a specific value. The proposed method constructs data-dependent inner and outer confidence sets that aim to contain the true feature subset for which the expected or realized outcomes of these features exceed a specified threshold. We establish theoretical guarantees for the probability that these confidence sets contain the true feature subset, both asymptotically and for finite sample sizes. The framework is validated through simulations and applied to real-world datasets, demonstrating its utility in contexts such as housing price prediction and time to sepsis diagnosis in healthcare. This approach provides a unified method for uncertainty quantification that is broadly applicable across various continuous and binary prediction models.