Redefining Pixel Circuit Analysis: Causal Discovery and Probabilistic Modeling

Abstract

When analyzing data using existing machine learning models without explicit causal information, several limitations often arise, particularly in the misinterpretation of correlations as causal relationships. These limitations are more pronounced in complex scenarios or in situations where outcomes are influenced by sequential effects. This paper presents an advanced methodology for analyzing pixel circuit design and its driving conditions, a domain characterized by complex interactions and multiple variables. Traditional machine learning methods, when applied in isolation, have shown limitations in unraveling the intricate causal relationships inherent in such systems. To address this challenge, we integrated Explainable AI (XAI) techniques, particularly SHAP (SHapley Additive exPlanations) values, into our analysis. In collaboration with domain experts, we constructed a Directed Acyclic Graph (DAG) that effectively reduced the complexity of interconnections and ensured consistency with empirical data. This approach facilitated a more accurate identification of the impact of each parameter and its causal influence. By decomposing the joint distribution of the variables into conditional distributions, taking into account their parental relationships, we gained a deeper understanding of the changes in causal mechanisms. Our methodology significantly enhanced the accuracy of causal analysis under realistic pixel driving conditions. The findings not only offer novel insights into pixel circuitry but also demonstrate the efficacy of combining machine learning with XAI in complex systems analysis, gaining wide acceptability among relevant experts.