This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from the data in literature. As the number of causal relationships is usually much smaller than that of associations, the proposed techniques will achieve explainability by making causes and effects interpretable to psychologists.
We envisage that the combination of causality and deep learning will lead to trustworthy and explainable models for psychologists and clinicians to formulate and verify their hypotheses more efficiently, predict risks more reliably, and identify previously unknown risk factors from a vast amount of data in a reasonable time, leading to better health care for patients.
The project aims to
- Design and develop a system to integrate mental health data in different formats from various sources.
- Develop cutting-edge deep causal models to identify causal relationships between risk factors and diseases by using data from mental health institutions.
- Devise the models that can produce explanations that are easily understandable by health professionals.
