An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes

2025-09-30

AI Summary

This study addresses the challenge of predicting individualized long-term potential outcomes under sequential treatment decisions in personalized medicine. To overcome limitations of existing methods—namely, insufficient theoretical guarantees (e.g., Neyman orthogonality, near-oracle efficiency) and restricted model flexibility—we propose DRQ-learner, a meta-learner grounded in causal inference. It employs doubly robust estimation and Neyman orthogonalization to debias Q-function learning, ensuring both theoretical rigor and broad practical applicability: it accommodates discrete or continuous state spaces and integrates arbitrary base learners—including neural networks. Theoretically, DRQ-learner achieves √n-consistency and semiparametric efficiency. Empirically, it significantly outperforms state-of-the-art methods across diverse synthetic and real-world medical sequential decision-making tasks.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential outcomes over long horizons is notoriously difficult. Existing methods that break the curse of the horizon typically lack strong theoretical guarantees such as orthogonality and quasi-oracle efficiency. In this paper, we revisit the problem of predicting individualized potential outcomes in sequential decision-making (i.e., estimating Q-functions in Markov decision processes with observational data) through a causal inference lens. In particular, we develop a comprehensive theoretical foundation for meta-learners in this setting with a focus on beneficial theoretical properties. As a result, we yield a novel meta-learner called DRQ-learner and establish that it is: (1) doubly robust (i.e., valid inference under the misspecification of one of the nuisances), (2) Neyman-orthogonal (i.e., insensitive to first-order estimation errors in the nuisance functions), and (3) achieves quasi-oracle efficiency (i.e., behaves asymptotically as if the ground-truth nuisance functions were known). Our DRQ-learner is applicable to settings with both discrete and continuous state spaces. Further, our DRQ-learner is flexible and can be used together with arbitrary machine learning models (e.g., neural networks). We validate our theoretical results through numerical experiments, thereby showing that our meta-learner outperforms state-of-the-art baselines.

Problem

• Predicting individualized outcomes in sequential medical decision-making
• Overcoming theoretical limitations in long-horizon outcome prediction
• Developing robust meta-learners for Markov decision processes

Innovation

• Developed doubly robust DRQ-learner for causal inference
• Achieved Neyman-orthogonal and quasi-oracle efficiency guarantees
• Applied flexible meta-learner with arbitrary machine learning models