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 …

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

Drawing causal inferences from observational studies (OS) requires unverifiable validity
assumptions; however, one can falsify those assumptions by benchmarking the OS with …

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 …

Background Due to the heterogeneity of low‐grade gliomas (LGGs), the lack of randomized
control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is …

Estimation of treatment efficacy of real-world clinical interventions involves working with
continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite …

Missing data is a systemic problem in practical scenarios that causes noise and bias when
estimating treatment effects. This makes treatment effect estimation from data with …

Motivation Estimating the effects of interventions on patient outcome is one of the key
aspects of personalized medicine. Their inference is often challenged by the fact that the …

Background This study focused on minimizing the costs and toxic effects associated with
unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing …

We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event
data in the presence of competing events. Albeit its great practical relevance, this problem …