As healthcare systems around the world face challenges such as escalating costs, limited
access, and growing demand for personalized care, artificial intelligence (AI) is emerging as …

Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …

Glaucoma causes irreversible blindness. In 2020, about 80 million people worldwide had
glaucoma. Existing machine learning (ML) models are limited to glaucoma prediction, where …

Good decision-making is a complex endeavor, and particularly so in a health context. The
possibilities for day-to-day clinical practice opened up by AI-driven clinical decision support …

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …

Personalized treatment effect estimates are often of interest in high-stakes applications–
thus, before deploying a model estimating such effects in practice, one needs to be sure that …

The machine learning (ML) toolbox for estimation of heterogeneous treatment effects from
observational data is expanding rapidly, yet many of its algorithms have been evaluated …

While much attention has been given to the problem of estimating the effect of discrete
interventions from observational data, relatively little work has been done in the setting of …

The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …