AIConfPaper
← Search

Jonas Schweisthal

10 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

Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation

Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel
ICLR 2026poster

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-called meta-learners) often perform poorly in the presence of low overlap. In this work, we introduce a new approach to t…

Cited by 0Source ↗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

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding

Yuchen Ma, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel
NeurIPS 2025poster

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 estimating treatment effects are typically trained on well-structured medical datasets that contain detailed patient informat…

Cited by 0Source ↗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

Treatment Effect Estimation for Optimal Decision-Making

Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Mihaela van der Schaar, Stefan Feuerriegel
NeurIPS 2025poster

Decision-making across various fields, such as medicine, heavily relies on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether the estimated CATE is positive, even though the decision-making performance of modern CATE estimators is poorly understo…

Cited by 0Source ↗Scholar ↗
2024

DiffPO: A causal diffusion model for learning distributions of potential outcomes

Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal, Stefan Feuerriegel
NeurIPS 2024poster

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 causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantific…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments

Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel
ICML 2024poster

Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments, such as different hospitals, physicians, or count…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

Reliable Off-Policy Learning for Dosage Combinations

Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
NeurIPS 2023poster

Decision-making in personalized medicine such as cancer therapy or critical care must often make choices for dosage combinations, i.e., multiple continuous treatments. Existing work for this task has modeled the effect of multiple treatments independently, while estimating the joint effect has recei…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗