CRAFT: Counterfactual Utility-Guided Dynamic Reasoning for Traditional Chinese Medicine Prescription Recommendation

Traditional Chinese Medicine (TCM) prescription recommendation is commonly formulated as static multi-label prediction, overlooking how therapeutic demands evolve during prescription construction. To address this limitation, we propose CRAFT, a Counterfactual utility-guided dynamic Reasoning framework with Adaptive Fusion for TCM prescription recommendation. CRAFT first performs hierarchical graph reasoning over symptom–symptom, symptom–herb, and herb–herb relations to capture structured clinical dependencies. It then reformulates prescription generation as an order-free dynamic decision process, where each step evaluates candidate herbs according to their neural compatibility and Counterfactual Marginal Utility (CMU). CMU performs candidate-level what-if evaluation by estimating the additional coverage of residual therapeutic demand if a candidate herb were added to the current partial prescription. CRAFT adaptively integrates this utility into neural decision scores and progressively constructs the prescription until a learned STOP action is selected. Experiments on two benchmark TCM prescription datasets demonstrate consistent improvements over competitive baselines, while further analyses show that CMU provides informative candidate-level signals throughout the dynamic reasoning process. The source code is publicly available at https://anonymous.4open.science/r/CRAFT-DAD6/.