Counterfactual Explanations in Personal Informatics for Personalized Mental Health Management

Personal informatics has been widely used to support users' mental health management by identifying factors associated with health indicators from mobile data. However, it remains challenging for users to develop data-driven coping strategies for mental health issues, especially when targeting specific situations involving multiple factors. To address this, we suggest an analysis pipeline for investigating counterfactual scenarios using mobile data. We also show how the pipeline generates counterfactuals from an open dataset, illustrating the feasibility of this approach in providing practical guidelines for each unique situation. Moreover, we discuss several considerations for integrating the proposed pipeline into personal informatics systems.