Uncovering Causal Factors Influencing Hog Prices: A Deep Granger Causality Inference Model for Multivariate Time Series Dynamics

The swine industry is vital to economic stability and household welfare in China and worldwide but remains highly vulnerable to price volatility driven by multiple factors. Capturing the underlying mechanisms of hog price formation is particularly challenging, as conventional models often fail to represent its nonlinear structures and complex multivariate causal dependencies. This study proposes a Deep Granger Causality Inference (DGCI) model that integrates deep learning with causal inference to identify the key driving factors of hog price dynamics. The DGCI model contains a Feature Reconstruction Module (FRM) and a Granger Causality Module (GCM). The FRM integrates a Variational Autoencoder (VAE) with a Transformer to capture latent temporal representations of multivariate variables. Meanwhile, the GCM quantifies nonlinear Granger causality strength by systematically excluding features to measure their causal impact on hog price. Furthermore, this study proposes the Causal Feature Importance (CFI) metric, which jointly evaluates reconstruction fidelity and causal strength to identify key determinants. To evaluate the model performance, this study utilizes a real-world hog dataset from China. The results demonstrate considerable gains, with DGCI decreasing MSE by 17.59% to 39.22% and MSPE by 32.35% to 54.90% relative to baseline models. The DGCI model highlights pork price, piglet cost, and slaughter volume as the primary determinants of hog price, with CFI values of 1.5216, 1.4451, and 1.4266, respectively. By advancing understanding of the causal drivers of price volatility, this study contributes to informed decision-making, enhanced food security, and the sustainable development of the swine industry. Moreover, as a generalizable methodology, the proposed framework can be broadly applied to analyze the influencing factors of other agricultural and livestock products.