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Konstantin Hess

11 accepted papers

2026

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes

Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Dennis Frauen, Stefan Feuerriegel
ICLR 2026poster

Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to give to a cancer patient). However, predicting potential out- comes over long horizons is notoriously difficult. Existi…

Cited by 0Source ↗code ↗Scholar ↗
2026

Efficient and Sharp Off-Policy Learning under Unobserved Confounding

Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
ICLR 2026poster

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 policy learning assumes unconfoundedness, meaning that no unobserved factors influence both treatment assignment and out…

Cited by 0Source ↗code ↗Scholar ↗
2026

IGC-Net for conditional average potential outcome estimation over time

Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
ICLR 2026poster

Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural…

Cited by 10Source ↗code ↗Scholar ↗
2026

Overlap-weighted orthogonal meta-learner for treatment effect estimation over time

Konstantin Hess, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel
ICLR 2026poster

Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases exponentially with longer prediction horizons. Thus, the observed data contain little support for many plausible treatment seq…

Cited by 0Source ↗code ↗Scholar ↗
2025

Conformal Prediction for Causal Effects of Continuous Treatments

Maresa Schröder, Dennis Frauen, Jonas Schweisthal, Konstantin Hess, Valentyn Melnychuk, Stefan Feuerriegel
NeurIPS 2025poster

Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal predi…

Cited by 0Source ↗code ↗Scholar ↗
2025

Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets

Yuxin Wang, Maresa Schröder, Dennis Frauen, Jonas Schweisthal, Konstantin Hess, Stefan Feuerriegel
ICLR 2025poster

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 records typically come from different hospitals, thus raising the question of how multiple observational/experimental datas…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Learning Representations of Instruments for Partial Identification of Treatment Effects

Jonas Schweisthal, Dennis Frauen, Maresa Schröder, Konstantin Hess, Niki Kilbertus, Stefan Feuerriegel
ICML 2025poster

Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (potentially high-…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

Model-agnostic meta-learners for estimating heterogeneous treatment effects over time

Dennis Frauen, Konstantin Hess, Stefan Feuerriegel
ICLR 2025poster

Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. Existing works for this task have mostly focused on *model-based* learners that adapt specific machine-learning models and adjustment mechanisms. In contrast, model-agnostic lear…

Cited by 3Source ↗PDF ↗Scholar ↗
2025

Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data

Dennis Frauen, Maresa Schröder, Konstantin Hess, Stefan Feuerriegel
NeurIPS 2025poster

Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event data with censored outcomes (e.g., due to study dropout). In this paper, we propose a toolbox of orthogonal survival lea…

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2025

Stabilized Neural Prediction of Potential Outcomes in Continuous Time

Konstantin Hess, Stefan Feuerriegel
ICLR 2025poster

Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confo…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation

Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
ICLR 2024poster

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 outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗