322 citations · 326 across the 18 of their papers we have counts for
7 papers · 1 filter
Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
Yuxin Wang, Dennis Frauen, Jonas Schweisthal +2
Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons. When dropout is informative (i.e., dependent on survival time…
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal +3
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to…
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal +1
The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-c…
LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Yuchen Ma, Dennis Frauen, Jonas Schweisthal +1
Estimating treatment effects is crucial for personalized decision-making in medicine, but this task faces unique challenges in clinical practice. At training time, models for estim…
A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
Yuchen Ma, Jonas Schweisthal, Hengrui Zhang +1
In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make op…
Treatment Effect Estimation for Optimal Decision-Making
Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal +2
Decision-making across various fields, such as medicine, heavily relies on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether…
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal +1
End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its efficiency remained unclear. Here,…