Individualized Treatment Effect on Factorized Multi-Domain Outcomes

Music City Center 

Personalized medicine encounters substantial challenge in mental health due to the subjective and diversified nature of the disease symptoms measured through multi-domain outcomes. Relying on a single summary measure for decision-making risks improving one symptom domain at the expense of another, underscoring the need for reliable effect estimation across multiple outcomes and various factors simultaneously. We propose a novel framework for learning individualized treatment effects with item response outcomes. This approach employs factor analysis to extract key disease factors from observed outcomes, leveraging them to construct a distributionally robust learning procedure. By jointly evaluating multi-domain treatment effects, the framework guarantees robust performance across a wide range of clinically relevant outcomes. Our method offers a computationally efficient algorithm with theoretical justification for simultaneously estimating factor loadings and treatment effects. Demonstrated in a randomized clinical trial for Major Depressive Disorder, it exhibits superior generalizability to external outcomes, underscoring its potential for advancing precision psychiatry.

Adversarial learning

Distributional robust

Item response data

Latent factor model

Mental disorders

Precision medicine 

Mental Health Statistics Section