Interpretable Machine Learning for Prediction with Missing Values at Test Time

Doktorsavhandling, 2025

This thesis addresses the challenge of making interpretable predictions when feature values may be missing at deployment (at "test time''). Although imputation is a common strategy for handling missing values, it can obscure the relationship between inputs and predictions, thereby undermining interpretability and trust -especially in safety-critical domains such as healthcare. Alternatively, incorporating missingness indicators may introduce complexity and further reduce model interpretability. Tree-based models can handle missing values natively but are limited to specific model classes, potentially restricting flexibility and generalizability. To overcome these limitations, this thesis develops methods that (i) retain or improve predictive performance, (ii) handle missing values effectively at test time, and (iii) produce models that are simple and interpretable.


We first leverage missingness patterns by introducing Sharing Pattern Submodels, where a separate interpretable submodel is trained for each unique missingness pattern, with parameters shared across submodels via sparsity to enhance generalization. Next, we investigate training models that rarely require the values of missing (or imputed) features at test time. We introduce MINTY, a linear rule-based model that avoids imputation by allowing logical substitutions for missing features. We then generalize this idea through a missingness-avoiding framework, which extends to multiple model classes, including decision trees, sparse linear models, and ensembles, by incorporating classifier-specific regularization terms into their learning objectives to discourage reliance on missing values. To support the development of clinically valuable models, we conducted a clinician survey revealing that medical professionals favor models that natively handle missingness. Finally, we explore interpretable patient history representations for modeling policies in sequential clinical decision-making, shifting the focus from missingness to temporal modeling. Collectively, this work establishes methods for interpretable machine learning with test-time missingness, supported by both technical innovations and human-centered insights, to enable transparent and practical decision support.