A Comprehensive Review of Convolutional Autoencoder with Dual-Key Transformer Network-Based Causality Analysis of Human Resource Practices on Firm Performance

Abstract

The increasing integration of artificial intelligence in human resource management has enabled organizations to uncover complex relationships between HR practices and firm performance. This paper presents a comprehensive review of convolutional autoencoder models combined with dual-key transformer networks for causality analysis in HR systems. The proposed hybrid framework leverages convolutional autoencoders for efficient feature extraction and dimensionality reduction, while dual-key transformer architectures enhance contextual understanding and temporal dependency modeling. The study emphasizes how these models can identify causal relationships rather than mere correlations, enabling more accurate predictions of organizational outcomes such as productivity, employee retention, and operational efficiency. Furthermore, the review examines the role of advanced attention mechanisms and representation learning in capturing latent HR patterns across structured and unstructured datasets. The integration of causality-driven learning frameworks improves decision-making processes in business environments by providing interpretable and reliable insights. This research also highlights the challenges associated with data heterogeneity, model complexity, and interpretability. The findings suggest that combining deep learning architectures with causal inference techniques significantly enhances the analytical capabilities of HR systems, paving the way for intelligent and adaptive workforce management solutions in modern enterprises.