This paper introduces the DRQ-learner, a novel causal inference meta-learner designed to predict individualized outcomes in Markov Decision Processes (MDPs). While traditional methods often struggle with the "curse of horizon" or lack theoretical stability, this new approach provides a foundation for more reliable personalized medicine and sequential decision-making. The authors leverage statistical orthogonality to ensure the model remains robust against errors in secondary estimation tasks and model misspecification. Through its doubly robust and quasi-oracle efficient properties, the learner performs as effectively as if the true underlying data distributions were already known. Empirical tests in simulated environments confirm that the DRQ-learner outperforms existing baselines, particularly in complex scenarios with low data overlap and long-term horizons. Ultimately, the research bridges the gap between causal treatment effect estimation and reinforcement learning to enhance patient-specific therapeutic strategies.
