Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

Flexible machine learning tools are being used increasingly to estimate heterogeneous
treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal …

Objective Develop and evaluate a treatment matching algorithm to predict differential
treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT …

Randomized controlled trials (RCTs) are often run in settings with many subpopulations that
may have differential benefits from the treatment being evaluated. We consider the problem …

Around the world, suicide ideation, attempts, and deaths pose a major public and mental
health challenge for patients (and their loved ones). Accordingly, there is a clear need for …

Suicide is a pressing public health concern in the US and the last decades have seen rising
suicide rates, but also increased research focused on the development and evaluation of …

The potential harms related to interventions for adults with suicide-related risk, particularly
hospitalization, have been well documented. Much less work has focused on the potential …

How best to understand and characterize causality is an age-old question in philosophy. As
such, one might expect that any discussion of causal inference would need to be framed in …

Large language models (LLMs) are increasingly used in daily work. In this paper, we
analyze whether training in prompt engineering can improve the interactions of users with …

The following chapter begins with Betty's story—an anonymised account provided with
Betty's consent. The chapter explores what a compassionate approach to listening, hearing …