MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values
This addresses the issue of missing data in interpretable rule models for tabular prediction tasks, offering a trade-off between fit, interpretability, and robustness, though it is incremental as it builds on existing rule-based approaches.
The paper tackles the problem of rule-based models being undefined or ambiguous when inputs are missing, which forces reliance on imputation and undermines interpretability, by proposing MINTY, a method that learns concise rule models to avoid depending on features with missing values, resulting in predictive performance comparable or favorable to baselines with reduced reliance on imputation.
Rule models are often preferred in prediction tasks with tabular inputs as they can be easily interpreted using natural language and provide predictive performance on par with more complex models. However, most rule models' predictions are undefined or ambiguous when some inputs are missing, forcing users to rely on statistical imputation models or heuristics like zero imputation, undermining the interpretability of the models. In this work, we propose fitting concise yet precise rule models that learn to avoid relying on features with missing values and, therefore, limit their reliance on imputation at test time. We develop MINTY, a method that learns rules in the form of disjunctions between variables that act as replacements for each other when one or more is missing. This results in a sparse linear rule model, regularized to have small dependence on features with missing values, that allows a trade-off between goodness of fit, interpretability, and robustness to missing values at test time. We demonstrate the value of MINTY in experiments using synthetic and real-world data sets and find its predictive performance comparable or favorable to baselines, with smaller reliance on features with missing values.