Causal reinforcement learning for personalized adaptive interventions in mild cognitive impairment

Mild cognitive impairment (MCI) prevention demands personalized, adaptive strategies, yet current clinical guidelines rely on static, experience-based approaches. We developed a causal reinforcement learning (CRL) framework integrating individual treatment effect (ITE) estimation via T-learner with Conservative Q-Learning (CQL) to optimize adaptive MCI interventions in continuous action spaces. Data were derived from a Sequential Multiple Assignment Randomized Trial (SMART) involving 61 participants (mean age 71.0 ± 6.0 years; 67.2% female) across three modalities: Virtual Reality Taichi, Offline Taichi, and Computerized Cognitive Training.

Baseline cognitive scores, comorbidities, and sociodemographic factors defined the state space; ITEs served as reward signals. In an exploratory continuous-action-space analysis, CRL-derived strategies were estimated to improve cognitive outcomes by an average of 1.03 Memory Guard score points over dynamic treatment regimens across four machine learning algorithms (all P