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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Related Experiment Video

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Ensemble of Temporal Weighting, Causal Inference, and Hierarchical Attribution towards SHAP Optimization.

Archana Salaria1, Manik Rakhra1, Nonita Sharma2

  • 1School of Computer Science Engineering, Lovely Professional University.

Journal of Visualized Experiments : Jove
|December 8, 2025
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Summary

This study introduces TCHSHAP, a novel Explainable Artificial Intelligence (XAI) framework that enhances model interpretability by prioritizing current data through temporal weighting and causal inference. The new method improves prediction accuracy and user trust in complex data-driven applications.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • The increasing complexity of data-driven models necessitates advanced Explainable Artificial Intelligence (XAI) techniques.
  • Traditional XAI methods often struggle with interpreting dynamic relationships within data.
  • Explainable AI (XAI) is crucial for transparency and trust in AI-driven predictions.

Purpose of the Study:

  • To propose a novel Ensemble SHapley Additive exPlanations (SHAP) framework, TCHSHAP, designed to improve the interpretability of dynamic relations.
  • To integrate temporal weighting, causal inference, and hierarchical attribution for enhanced model explanation.
  • To validate the efficacy of TCHSHAP in improving transparency and interpretability without compromising model performance.

Main Methods:

  • Development of the TCHSHAP framework incorporating temporal weighting (exponential decay), causal inference, and hierarchical attribution.
  • Application of data preprocessing techniques including one-hot encoding, min-max scaling, and Interquartile Range outlier removal.
  • Experimental evaluation using a Random Forest model on a crop yield dataset, comparing traditional SHAP with the proposed TCHSHAP.

Main Results:

  • The TCHSHAP model demonstrated an improvement in average prediction from 161.137 (traditional SHAP) to 161.506, highlighting the effectiveness of temporal and causal significance.
  • Hierarchical attribution revealed that agricultural features have the most significant impact on the target variable, followed by geographical and environmental factors.
  • The proposed approach enhances both global and local interpretability, thereby increasing user confidence in model predictions.

Conclusions:

  • TCHSHAP effectively improves transparency and interpretability in complex data-driven applications, particularly in regression modeling.
  • The framework enhances user trust by providing clearer insights into model behavior and feature importance.
  • TCHSHAP offers a viable solution for interpretable AI in real-world scenarios without sacrificing predictive performance.