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

Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …

In the era of fast-paced precision medicine, observational studies play a major role in
properly evaluating new treatments in clinical practice. Yet, unobserved confounding can …

Treatment effect estimation in continuous time is crucial for personalized medicine.
However, existing methods for this task are limited to point estimates of the potential …

Unobserved confounding is common in many applications, making causal inference from
observational data challenging. As a remedy, causal sensitivity analysis is an important tool …

Counterfactual inference aims to answer retrospective" what if" questions and thus belongs
to the most fine-grained type of inference in Pearl's causality ladder. Existing methods for …

State-of-the-art methods for conditional average treatment effect (CATE) estimation make
widespread use of representation learning. Here, the idea is to reduce the variance of the …

Inferring causal effects of continuous-valued treatments from observational data is a crucial
task promising to better inform policy-and decision-makers. A critical assumption needed to …

A largely unaddressed problem in causal inference is that of learning reliable policies in
continuous, high-dimensional treatment variables from observational data. Especially in the …