Causal inference of whole-grain foods' risk based on a generative adversarial network and Bayesian network

Whole-grain foods (WGFs) constitute a large part of humans' daily diet, making risk identification of WGFs important for health and safety. However, existing research on WGFs has paid more attention to revealing the effects of a single hazardous substance or various hazardous substances on food safety, neglecting the mutual influence between individual hazardous substances and between hazardous substances and basic information. Therefore, this paper proposes a causal inference of WGFs' risk based on a generative adversarial network (GAN) and Bayesian network (BN) to explore the mutual influence between hazardous substances and basic information. The experiment results show that the proposed GAN outperformed several traditional data-imputation methods, producing at least a 13.65% reduction of the root mean square error (RMSE). The classification accuracy of the BN model reached 91%. In conclusion, we distinguish the provinces, periods, food categories, and hazardous substances cause the absolute risk of WGFs and the high risk of mycotoxins and compounds (MaCs) and cadmium. PRACTICAL APPLICATION: This research can be applied to impute missing values for whole-grain foods (WGFs) sampling data, and explore the causality among hazardous substances themselves, that between hazardous substances and basic information in WGFs. Additionally, it can be applied to infer root cause of existing or potential WGFs risk (e.g., provinces, periods, food categories, and hazardous substances).