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

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

Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …

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 …

We investigate how to exploit structural similarities of an individual's potential outcomes
(POs) under different treatments to obtain better estimates of conditional average treatment …

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 …

Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug
discovery and development, clinical trials, personalized medicine, pharmacogenomics …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …