Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty

2025-02-10

AI Summary

This work addresses strategic classification under settings where agents can manipulate features, features exhibit causal dependencies, and agents possess incomplete information about both the classifier and the underlying causal structure. The goal is to incentivize agents to exert effort on socially beneficial features rather than on gaming the classifier. Method: We introduce the first integration of causal graph models into an incomplete-information game-theoretic framework for strategic classification. Contribution/Results: We theoretically characterize equilibrium effort allocations under both complete and incomplete information, revealing a fundamental “variance–importance trade-off” governing effort distribution. We derive necessary and sufficient conditions for classifier design that elicit socially optimal effort, and prove solvability for several canonical cases. Empirical evaluation on cardiovascular risk prediction data demonstrates that our mechanism effectively steers agent effort toward desirable, welfare-enhancing features.

Technology Category

• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Causality

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We study strategic classification in binary decision-making settings where agents can modify their features in order to improve their classification outcomes. Importantly, our work considers the causal structure across different features, acknowledging that effort in a given feature may affect other features. The main goal of our work is to understand emph{when and how much agent effort is invested towards desirable features}, and how this is influenced by the deployed classifier, the causal structure of the agent's features, their ability to modify them, and the information available to the agent about the classifier and the feature causal graph. In the complete information case, when agents know the classifier and the causal structure of the problem, we derive conditions ensuring that rational agents focus on features favored by the principal. We show that designing classifiers to induce desirable behavior is generally non-convex, though tractable in special cases. We also extend our analysis to settings where agents have incomplete information about the classifier or the causal graph. While optimal effort selection is again a non-convex problem under general uncertainty, we highlight special cases of partial uncertainty where this selection problem becomes tractable. Our results indicate that uncertainty drives agents to favor features with higher expected importance and lower variance, potentially misaligning with principal preferences. Finally, numerical experiments based on a cardiovascular disease risk study illustrate how to incentivize desirable modifications under uncertainty.

Problem

• Study strategic classification in binary decision-making settings.
• Analyze agent effort investment influenced by causal structures.
• Explore classifiers' role in incentivizing desirable feature modifications.

Innovation

• Causal feature structure analysis
• Non-convex classifier design
• Uncertainty-driven effort selection