A Novel Approach for Constructing Personalized Networks from Longitudinal Perceived Casual Relations

Personalized networks are used to tailor psychological interventions to an individual, however, the estimation of these networks is limited by strong statistical assumptions. We introduce a new method to construct networks based on longitudinal perceived causal relation assessments. Results show that LPCR produces stable and valid networks, representing patients' experiences.