CausalSymptom: Learning Causal Disentangled Representation for Depression Severity Estimation on Transcribed Clinical Interviews

Depression is one of the most prevalent mental disorders in the world. Although some researchers have applied deep learning to assess depression, understanding its pathogenesis remains challenging. Psychological research highlights the inherent correlations among several depression symptoms. However, existing methods often ignore causal connections, treating symptoms independently and relying on local relationships without identifying valid causal paths. In this paper, we propose a novel framework, CausalSymptom, based on a variational autoencoder, to predict the severity of depression and explore its pathogenesis. The framework incorporates causal representation learning to model latent symptom states. Crucially, we provide theoretical evidence that these symptom variables can be disentangled from others by using additional information as supervision. Once these representations are learned, we present a new framework that includes a novel module, the Graph Causal Effect Network (GCEN), for the estimation of depression. This module learns a causal graph representation based on a causal total effect mechanism, which integrates causal influences across all paths, leading to a more accurate estimation of depression severity. Our model achieves competitive performance on the DAIC-WOZ and E-DAIC datasets. Furthermore, the experimental study empirically shows that our model offers insight into the pathogenesis of depression.