The increasing reliance on machine learning in high-stakes domains such as healthcare, finance, and public policy necessitates models that are not only accurate but also interpretable and causally robust. This study presents a hybrid framework that integrates explainable artificial intelligence (XAI) techniques with causal inference methods to enhance both the transparency and the reliability of machine learning systems.

The proposed framework combines SHAP (Shapley Additive Explanations) for localized interpretability, counterfactual reasoning to support decision justification, and ensemble learning techniques including XGBoost and LightGBM for high-performance prediction. To further enrich the interpretability in complex data scenarios, the model introduces a novel integration of Large Language Models (LLMs) into structured tabular environments through a statistical vector fusion approach.

As a practical demonstration, a synthetic dataset simulating personalized healthcare resource allocation (e.g., ICU prioritization) is constructed based on real-world statistical distributions from OECD and WHO health indicators. Features include clinical variables (e.g., comorbidity index, age, oxygen level) and social determinants (e.g., income level, regional density). The model is evaluated based on both predictive performance (AUC, F1-score) and interpretability metrics (feature attribution stability, causal graph coherence).

Initial results indicate that the hybrid approach not only improves model transparency but also supports ethically aligned and causally coherent decision-making, which is essential for applications where decisions have critical real-life consequences. The framework contributes to the growing literature on explainable and reliable machine learning, offering both theoretical advancements and practical pathways for integration into digital decision support systems.