Bridging Probabilistic Graphical Models and Deep Generative Networks for Improved Causal Inference and Uncertainty Quantification in Data Science
Keywords:
Causal Inference, Probabilistic Graphical Models, Deep Generative Networks, Uncertainty Quantification, Hybrid Models, Representation LearningAbstract
The intersection of causality and machine learning has witnessed rapid advancement, yet a persistent challenge lies in integrating explicit causal reasoning with the flexibility of deep generative models. This paper presents a framework that bridges Probabilistic Graphical Models (PGMs) and Deep Generative Networks (DGNs) to enhance causal inference and uncertainty quantification in complex, high-dimensional data environments. Drawing from the strengths of both paradigms—interpretable structure and principled inference from PGMs and expressive representation learning from DGNs—this hybrid model addresses limitations in counterfactual estimation and epistemic uncertainty prevalent in deep learning systems. We demonstrate through theoretical integration and synthetic experiments how this framework enhances robustness, interpretability, and decision-making capacity in real-world data science applications.
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