Evaluating Surrogates in Individual Treatment Regimes

2025-11-29

AI Summary

In individualized treatment rule (ITR) learning, the primary outcome is often costly and delayed, necessitating reliance on surrogate variables for decision-making. However, conventional surrogate validity criteria—such as strong correlation—fail to guarantee optimal ITR decisions, particularly under budget constraints. To address this, we propose the first surrogate endpoint evaluation framework specifically designed for ITR learning, introducing three novel metrics: *surrogate regret* (decision bias), *surrogate gain* (performance improvement under budget constraints), and *surrogate efficiency* (benefit per unit cost). Leveraging doubly robust estimation, our framework integrates causal inference with statistical learning to yield an asymptotically normal estimator with valid statistical inference. Theoretical analysis, extensive simulations, and real-data applications demonstrate that our approach substantially outperforms evaluation strategies based solely on correlation or classical surrogate criteria in terms of decision optimality, resource efficiency, and inferential reliability.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Decision/Utility Theory
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In many decision-making problems, the primary outcome of interest is costly or time-consuming to observe, prompting the use of surrogate variables to learn individualized treatment rules (ITRs). However, even when a surrogate is strongly correlated with the outcome or satisfies conventional surrogate validity conditions, surrogate-based ITRs may diverge from outcome-optimal decisions - particularly under realistic budget constraints. To address this gap, we develop a framework for evaluating the decision-making value of surrogate endpoints. We introduce three ITR-oriented performance metrics: surrogate regret, which measures the utility loss from using surrogate-based ITRs instead of outcome-optimal ITRs; surrogate gain, which quantifies the benefit of surrogate-based treatment decisions relative to no treatment; and surrogate efficiency, which evaluates improvement over random treatment assignment. We further extend these metrics to budget-constrained settings and propose doubly robust estimators. We establish their asymptotic properties and provide valid statistical inference. Simulation and real data studies demonstrate the effectiveness of the proposed approach. Overall, this work offers the first principled framework to rigorously evaluate surrogates, providing a promising pathway toward more efficient decision-making.

Problem

• Evaluating surrogate endpoints in individualized treatment regimes
• Measuring utility loss and benefit of surrogate-based decisions
• Extending evaluation to budget-constrained settings with robust estimators

Innovation

• Framework evaluates surrogate endpoints for decision-making value
• Introduces three ITR-oriented metrics: regret, gain, efficiency
• Extends metrics to budget constraints with doubly robust estimators