Causal Graph Learning for Face-Based Interpretable Hierarchical Diagnosis of Depression

作者：Baoliang Zhang, Dixin Wang, Mingmei Cheng, Xiaofeng Liu, Yanzhong Wang, Feng Zhu, Zhixiong Lin, Chuan Shi, Wanqing Xie · 发表于：IEEE Transactions on Computational Social Systems · 年份：2026 · DOI：10.1109/tcss.2025.3645183 · 研究领域：Emotion and Mood Recognition、Mental Health via Writing、Machine Learning in Healthcare

Depression has become one of the most serious mental illnesses, leading to a substantial decline in quality of life, an elevated risk of suicide, and significant societal challenges. Despite significant progress in the application of deep learning for depression diagnosis, most prevalent methods rely on correlative rather than causal features, limiting their accuracy and interpretability. Here, we propose a causal graph learning (CGL) method for the hierarchical diagnosis of depression. Specifically, we first construct a novel depression facial graph (DFGraph) structure based on a prior knowledge, which collects information about subjects’ facial cues. Our CGL model leverages the DFGraph structure and incorporates a built-in masking mechanism, which is designed to effectively differentiate causal features from confounding ones. It employs backdoor adjustment techniques, which control for confounding variables by blocking noncausal paths, to identify and select pertinent causal features, thereby enhancing the accuracy of the hierarchical diagnosis of depression. We conducted extensive experiments on the collected depression dataset. Our results show that the proposed method provides better results and interpretability is further improved compared to the publicly available baseline.