RoSHAP: Efficient Uncertainty Quantification for Feature Attributions

Feature attribution analysis is critical for interpreting machine learning models and supporting data-driven decisions. However, feature attribution values and rankings can vary substantially across train–test splits, random seeds, or model-fitting procedures. The nature of attribution uncertainty makes it challenging to assess the true importance of a feature. We introduce a robust distributional framework **RoSHAP** that treats feature attributions as random quantities. RoSHAP estimates the attribution distribution by refitting the predictive model across multiple data resamples. The resulting distribution captures both attribution magnitude and variability within a unified framework. We show that, under mild regularity conditions, the aggregated attribution score is asymptotically Gaussian, enabling a computationally efficient approach to achieve the desired estimation precision. Simulations and real-data experiments demonstrate that RoSHAP recovers the known feature ordering, quantifies uncertainty in feature importance, and achieves competitive signal identification and predictive performance relative to single-fit and refit-based approaches. By incorporating attribution stochasticity and a computationally efficient approach, RoSHAP provides a reliable and robust metric for assessing feature importance under uncertainty.