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

Summary

This article aims to assess the impact of temporal weighting, causal inference, and hierarchical attribution on interpretability optimization.

Abstract

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.

Introduction

Regression plays a major role in predictive analytics, encompassing several domains including climate modelling, financial forecasting, healthcare diagnostics, and agricultural yield estimation^1,2. However, the unexplained results of regression models, especially non-linear and high-capacity models like Gradient Boosting Machines (GBMs), Random Forests, and Deep Neural Networks (DNNs), pose significant challenges, particularly for applications requiring transparency and actionable insights. This interpretability gap can be bridged by employing Explainable Artificial Intelligence (XAI), a technique that is stil....

Protocol

NOTE: This section discusses the proposed ensemble method, including all the proposed amendments as illustrated in Figure 1.

Data preparation

In order to validate the effectiveness of the proposed framework, the authors carried out an experiment on the crop yield dataset collected from Kaggle¹⁷. This dataset consists of Indian agricultural data for various crops from 1997 to 2020 and was accessed in March 2025. This dataset comprises numerous features, namely season, crop_year, fertilizer, pesticide, crop, and yield (target variable), etc. The....

Results

This section discusses the results obtained by applying various methods used during the experiment study, comprising various subsections as follows:

Data collection and preprocessing

In order to perform an experimental evaluation of the proposed framework, a dataset regarding crop yield was collected from Kaggle¹⁹. The collected.......

Discussion

The prime objective of the current study is to incorporate temporal weighting, causal inference, and hierarchical significance in the traditional SHAP model, yielding the TCHSHAP model. The motive behind including these components in XAI techniques is to enhance the interpretability of the results. For temporal weighting, we have considered an exponential decaying technique, which gives more weightage to recent values in comparison to old values. In causal significance, authors try to assess the direct impact of various .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ELI5	0.13.0	PyPI
LIME	0.2.0.1	PyPI
Nvidia DGX A100 GPU Server	CPU Dual AMD Rome 7742, 128 cores total	Nvidia
System Memory 1TB
Python	3.1	Python
SHAP	0.41.0	PyPI
scikit-learn	1.3.0	PyPI
TensorFlow	2.13	Tensor Flow
XGBoost	1.7.6	PyPI