Many expert-domain decisions involve intermediate variables, yet standard LLM evaluation often measures only final-answer accuracy. We present a controlled study of intermediate-variable dependence in medical treatment selection, using diagnosis as an explicit variable between patient phenotype and treatment. We construct a six-option multiple-choice benchmark from PrimeKG-derived phenotype–disease–drug chains and evaluate six LLMs using behavioral diagnosis interventions, held-out QLoRA path supervision, external validation, and auxiliary layer-wise localization analysis. Providing the gold diagnosis improves average treatment accuracy from 49.9% to 84.9%, while incorrect predicted diagnoses reduce accuracy from 85.5% to 23.1%. A shuffled-diagnosis control yields 18.2% accuracy, suggesting that the effect depends on diagnosis correctness rather than merely adding disease-like text. Held-out QLoRA experiments show that path supervision improves treatment selection, with diagnosis-first full-chain and multitask objectives yielding the strongest gains. Center-of-Gravity localization further gives a modest diagnosis-first signal, which we interpret as a localization pattern rather than evidence of faithful clinical reasoning. Overall, in this controlled setting, final-answer accuracy can obscure failures in intermediate-variable dependence, while structured supervision can partially improve diagnosis-to-treatment behavior.

While it is widely agreed that large language models (LLMs) store concepts from multiple semantic hierarchies, much remains unknown regarding the structure of this storage. The correspondence between the functional roles of LLM components and the semantic hierarchies of knowledge remains underexplored in the current literature. For example, is information organized hierarchically within sections of an LLM? We take an initial step towards causally examining the correspondence between hierarchical concepts and the multi-granular structures (layers and attention heads) of various models. Specifically, we generate a dataset of semantic hierarchies and investigate their storage locations in six LLMs using activation patching, a causal intervention technique. At the layer level, our findings show a moderate indication that concepts at finer levels of granularity are stored around 61-78% of the time (p < 0.01) before those at coarser granularity. There is evidence for this trend at the attention level; however, the high variability in attention level results suggests that concepts are stored across attention heads rather than within. Our results offer insight into semantic organization within LLMs.

Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in decision-making and treatment planning. However, these models lack two crucial features that clinicians highly value: interpretability and interactivity. The “black-box” nature of these models makes it difficult for clinicians to understand the reasoning behind predictions, limiting their ability to make informed decisions. Additionally, the absence of interactive mechanisms prevents clinicians from incorporating their own knowledge and experience into the decision-making process. To address these limitations, we propose II-KEA, a knowledge-enhanced agent-driven causal discovery framework that integrates personalized knowledge databases and agentic LLMs. II-KEA enhances interpretability through explicit reasoning and causal analysis, while also improving interactivity by allowing clinicians to inject their knowledge and experience through customized knowledge bases and prompts. II-KEA is evaluated on both MIMIC-III and MIMIC-IV, demonstrating superior performance along with enhanced interpretability and interactivity, as evidenced by its strong results from extensive case studies.