322 citations · 381 across the 26 of their papers we have counts for
5 papers · 1 filter
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical…
DiffPO: A causal diffusion model for learning distributions of potential outcomes
Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal +1
Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of cau…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
Conformal Prediction for Causal Effects of Continuous Treatments
Maresa Schröder, Dennis Frauen, Jonas Schweisthal +3
Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which…
IGC-Net for conditional average potential outcome estimation over time
Konstantin Hess, Dennis Frauen, Valentyn Melnychuk +1
Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for thi…