Causal-Enhanced Wasserstein GAN in VAE Latent Space for Discrete Integer Data Generation

Imbalanced data distribution is a common challenge that hinders model performance across various industries, particularly due to the scarcity of faulty samples. Sample generation can help address this issue, but existing methods often overlook causal structures and dependencies, resulting in low-quality synthetic samples. Hence, a novel generative method, Causal-Enhanced Latent Space Wasserstein Generative Adversarial Network (CE-LSWGAN) with gradient penalty, is proposed for imbalanced discrete integer data. First, a causal directed acyclic graph is extracted using the Peter-Clark (PC) algorithm. The causal dependencies are then embedded into the latent space of a Variational Autoencoder (VAE) through graph propagation, creating a constrained structural causal prior. Next, a Generative Adversarial Network (GAN) is introduced in this structured latent space to train the latent variables of encoder. A gradient penalty is applied to ensure stable training. To further enhance the generation of minority class samples, a dynamic resampling strategy based on class weights is employed. Finally, within the VAE decoder, the GAN-generated latent variables are integrated with causal constraints, ensuring that generated samples adhere to causal logic. Experimental results demonstrate that CE-LSWGAN achieves an average causal consistency of 0.6088 and improves similarity between generated and original data by 18.27% compared to six state-of-the-art baselines. Downstream classifiers show substantial performance gains, with the F1-score increasing by up to 60% compared to the baseline models without data augmentation. The model attains precision and recall scores of 0.9351 and 0.9352, respectively. Overall, CE-LSWGAN demonstrates superior performance in generation quality, causal fidelity, and task adaptability, providing a reliable data augmentation framework for the causal-based applications.