Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis.

ABSTRACT:

Background

Digital mental health tools promise to enhance the reach and quality of care. Current tools often recommend content to individuals, typically using generic knowledge-based systems or predictive artificial intelligence (AI). However, predictive AI is problematic for interventional recommendations as cause-effect relationships can be confounded in observed data. Therefore, causal AI is required to compare future outcomes under different interventions.

Objective

We aimed to develop a causal AI recommendation system that uses an individual's current presentation, their preferences, and the learned dynamics between domains to rank interventions.

Methods

We frame the recommendation problem within a Bayesian decision-theoretic framework, whereby a preference order

SUBMITTER: Varidel M 

PROVIDER: S-EPMC12200822 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature