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

Journal: Journal of medical Internet research
Published Date: Jun 19, 2025

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.

Authors

  • Mathew Varidel
    Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Sydney, 2050, Australia, 61 0293510774.
  • Victor An
    Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Sydney, 2050, Australia, 61 0293510774.
  • Ian B Hickie
    Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
  • Sally Cripps
    Human Technology Institute, University of Technology Sydney, Sydney, Australia.
  • Roman Marchant
    Human Technology Institute, University of Technology Sydney, Sydney, Australia.
  • Jan Scott
    Professor at the Academic Psychiatry, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
  • Jacob J Crouse
    Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Sydney, 2050, Australia, 61 0293510774.
  • Adam Poulsen
    Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Sydney, 2050, Australia, 61 0293510774.
  • Bridianne O'Dea
    Institute for Mental Health and Wellbeing, Flinders University, Adelaide, Australia.
  • Sarah McKenna
    Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Sydney, 2050, Australia, 61 0293510774.
  • Frank Iorfino
    Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.

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