322 citations · 322 across the 13 of their papers we have counts for
15 papers · 1 filter
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4
Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences).…
Representation Learning for Exact Preimages
Konstantin Hess, Stefan Feuerriegel
Modern neural predictors can model highly nonlinear maps, but many scientific and engineering tasks require reasoning in the opposite direction: given a performance or safety level…
Causal methods for LLM development and evaluation
Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10
Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and evaluation pipelines. Here,…
Debiased neural operators for estimating functionals
Konstantin Hess, Dennis Frauen, Niki Kilbertus +1
Neural operators are widely used to approximate solution maps of complex physical systems. In many applications, however, the goal is not to recover the full solution trajectory, b…
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
Konstantin Hess, Dennis Frauen, Mihaela van der Schaar +1
Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases expone…
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
Dennis Frauen, Maresa Schröder, Konstantin Hess +1
Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event…
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
Konstantin Hess, Dennis Frauen, Valentyn Melnychuk +1
We develop a novel method for personalized off-policy learning in scenarios with unobserved confounding. Thereby, we address a key limitation of standard policy learning: standard…
Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
Yuxin Wang, Maresa Schröder, Dennis Frauen +3
Constructing confidence intervals (CIs) for the average treatment effect (ATE) from patient records is crucial to assess the effectiveness and safety of drugs. However, patient rec…
Learning Representations of Instruments for Partial Identification of Treatment Effects
Jonas Schweisthal, Dennis Frauen, Maresa Schröder +3
Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a st…
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…
Stabilized Neural Prediction of Potential Outcomes in Continuous Time
Konstantin Hess, Stefan Feuerriegel
Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to persona…
Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
Dennis Frauen, Konstantin Hess, Stefan Feuerriegel
Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. For example, electronic health records are commonly collec…
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…
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Konstantin Hess, Valentyn Melnychuk, Dennis Frauen +1
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 outcome…