Rationale: Shorter time-to-antibiotics improves survival from sepsis, particularly among
patients in shock. There may be other subgroups for whom faster antibiotics are particularly …

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about
2.8 million people worldwide. Disease course after the most common diagnoses of relapsing …

Similarly to other domains of the social sciences, behavioural science has grappled with a
crisis concerning the effect sizes of research findings. Different solutions have been …

Individualized treatment decisions can improve health outcomes, but using data to make
these decisions in a reliable, precise, and generalizable way is challenging with a single …

This paper conducts a methodological review of papers using the causal forest machine
learning method for flexibly estimating heterogeneous treatment effects. It examines 133 …

Existing evidence regarding the effects of Medicaid expansion, largely focused on
aggregate effects, suggests health insurance impacts some health, healthcare utilization …

Causal machine learning methods which flexibly generate heterogeneous treatment effect
estimates could be very useful tools for governments trying to make and implement policy …

The estimation of heterogeneous treatment effects has attracted considerable interest in
many disciplines, most prominently in medicine and economics. Contemporary research has …

For survival analysis applications we propose a novel procedure for identifying subgroups
with large treatment effects, with focus on subgroups where treatment is potentially …

Abstract Evaluation of the heterogeneous treatment effect (HTE) allows for the assessment
of the causal effect of a therapy or intervention while considering heterogeneity in individual …