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 …

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 …

The recent availability of electronic health records (EHRs) have provided enormous
opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has …

Digital twins, which are in silico replications of an individual and its environment, have
advanced clinical decision-making and prognostication in cardiovascular medicine. The …

Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in
healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series …

Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …

Consider the problem of improving the estimation of conditional average treatment effects
(CATE) for a target domain of interest by leveraging related information from a source …

In medical practice, treatments are selected based on the expected causal effects on patient
outcomes. Here, the gold standard for estimating causal effects are randomized controlled …

Decision-making in personalized medicine such as cancer therapy or critical care must often
make choices for dosage combinations, ie, multiple continuous treatments. Existing work for …

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant
given the increasing pressure on peak energy supply, particularly in Europe, where peak …