Strategic Feature Selection

2026-06-17

AI Summary

This work addresses the vulnerability of predictive models in high-stakes domains—such as healthcare—to strategic manipulation of input features, a challenge inadequately mitigated by existing coarse-grained feature filtering approaches. The authors propose a novel method that jointly optimizes feature selection and the regularization strength of ridge regression to enhance robustness against such strategic behavior. By integrating game-theoretic modeling with feature selection theory, they demonstrate that naively discarding features based solely on manipulability is often suboptimal and instead provide a refined characterization of the performance of feature subsets under strategic perturbations. Empirical validation on real-world healthcare payment data confirms the efficacy of the proposed algorithm, offering a principled and practical framework for designing coarse-grained policies resilient to strategic manipulation.

Abstract

When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The typical solution is to redesign the predictor itself to explicitly account for strategic interactions. In practice, however, decision makers are often constrained to adjusting coarser levers within existing prediction pipelines. For example, healthcare organizations often select which features to exclude based on perceived manipulability, while using standard regularization procedures to shrink the coefficients of retained features. In this work, we initiate a formal study of strategic classification through feature selection and its interaction with ridge regularization. Our main finding is that excluding individual features based on their manipulability alone is generally suboptimal. We provide a fine-grained characterization of the performance of a feature subset under optimal regularization, yielding new insights for policy design. Motivated by this characterization, we develop a practical algorithm for jointly choosing the feature set and the level of ridge regularization. Through a real-world case study on a healthcare payments benchmark, we illustrate how our algorithm can guide the design of coarse policy levers in practice. Our results provide a principled, practical framework for mitigating the effects of strategic behavior in algorithmic decision-making systems.

Problem

• strategic manipulation
• feature selection
• ridge regularization
• algorithmic decision-making
• healthcare

Innovation

• strategic classification
• feature selection
• ridge regularization
• algorithmic decision-making
• policy design