Ensemble of Temporal Weighting, Causal Inference, and Hierarchical Attribution towards SHAP Optimization

During the past few years, the need for transparency and interpretability has been intensified owing to significant advancements in data-driven models, leading to the emergence of Explainable Artificial Intelligence (XAI). Several traditional XAI approaches are prevalent; however, these have limited competence in interpreting dynamic relations. The current research aims to address this limitation by proposing a novel Ensemble SHapley Additive exPlanations (SHAP) framework that focuses on temporal weighting, causal inference, hierarchical attribution, and interpretability optimization referred to as TCHSHAP. TCHSHAP prioritizes current information over historical information by temporal weighting through exponential decay. Further, causal inference separates correlation from causality to gain practical insights. Additionally, hierarchical attribution allows insights at granular (region level) and aggregated levels (feature-group impacts). These approaches are integrated to achieve a more interpretable and explainable model. To validate the efficacy of the proposed model, we carry out an experiment on the crop yield dataset collected from Kaggle. Ahead of experimental evaluation, data preprocessing is performed using one-hot encoding. Data normalization is done by min-max scaling, and outliers are removed through the Interquartile range. For the sake of experimental evaluation, the authors used the SHAP XAI model for Random Forest. When assessing the efficacy of the proposed TCHSHAP model, it is observed that while the average prediction for traditional SHAP is 161.137, it escalates to 161.506 after incorporating temporal weighting and causal inference, advocating the effectiveness of employing temporal and causal significance. Additionally, during hierarchical attribution, it is observed that agricultural features have the strongest dominance over the target variable. This dominance is followed by geographical and environmental factors in order. Thus, the obtained results authorize the efficacy of the proposed approach towards enhancing the global and local interpretability, strengthening the user's trust in model predictions. The current work offers ways to improve transparency and interpretability without affecting model performance. The suggested model also enables interpretable and efficient regression modelling in complex, data-driven applications, enabling its widespread application in real-world settings.