1.3k citations · 3.2k across the 145 of their papers we have counts for
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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).…
Prediction-powered Neural Architecture Search
Pascal Janetzky, Yuxin Wang, Michael Klar +1
Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are limited because training and ev…
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…
OncoSynth: Synthetic data generation for treatment effect estimation in oncology
Octavia-Andreea Ciora, Julian Welzel, Dennis Frauen +6
In oncology, access to patient-level data is often restricted. Synthetic data provides an alternative for analyzing treatment effectiveness, but existing methods for synthetic data…
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,…
Adaptive Experimentation for Censored Survival Outcomes
Yuxin Wang, Dennis Frauen, Jonas Schweisthal +3
Adaptive experimentation enables efficient estimation of causal effects, but existing methods are not designed for survival data with censoring, where event times are only partiall…
Continual Learning of Domain-Invariant Representations
Pascal Janetzky, Tobias Schlagenhauf, Stefan Feuerriegel
Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL methods optimize for in-domain…
ConfoundingSHAP: Quantifying confounding strength in causal inference
Marie Brockschmidt, Santo M. A. R. Thies, Maresa Schröder +5
In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, the treatment assignment mech…
SkillGen: Verified Inference-Time Agent Skill Synthesis
Yuchen Ma, Yue Huang, Han Bao +5
Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, high-quality skills are still…
ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
Maresa Schröder, Pascal Janetzky, Michael Klar +1
Bayesian optimization is widely used for hyperparameter optimization when model evaluations are expensive; however, noisy acquisition estimates can lead to unstable decisions. We i…
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…
Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
Haorui Ma, Dennis Frauen, Valentyn Melnychuk +1
Estimation of heterogeneous long-term treatment effects (HLTEs) is relevant for personalized decision-making in marketing, economics, and medicine, where short-term observational d…
Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel +1
Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem. Howe…
Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework
Maresa Schröder, Miruna Oprescu, Stefan Feuerriegel +1
Estimating treatment effects in networks is challenging, as each potential outcome depends on the treatments of all other nodes in the network. To overcome this difficulty, existin…
Synthetic Interaction Data for Scalable Personalization in Large Language Models
Yuchen Ma, Yue Huang, Wenjie Wang +3
Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-leve…
Rank-Learner: Orthogonal Ranking of Treatment Effects
Henri Arno, Dennis Frauen, Emil Javurek +2
Many decision-making problems require ranking individuals by their treatment effects rather than estimating the exact effect magnitudes. Examples include prioritizing patients for…
Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach
Abdurahman Maarouf, Alket Bakiaj, Stefan Feuerriegel
Venture capital (VC) investments in early-stage startups that end up being successful can yield high returns. However, predicting early-stage startup success remains challenging du…
Nonparametric LLM Evaluation from Preference Data
Dennis Frauen, Athiya Deviyani, Mihaela van der Schaar +1
Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards. However, many existing approaches either rely on re…
Beyond Means: A Dynamic Framework for Predicting Customer Satisfaction
Christof Naumzik, Abdurahman Maarouf, Stefan Feuerriegel +1
Online ratings influence customer decision-making, yet standard aggregation methods, such as the sample mean, fail to adapt to quality changes over time and ignore review heterogen…
Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
Harry Amad, Zhaozhi Qian, Dennis Frauen +3
Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This ma…
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…
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…
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…
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
Valentyn Melnychuk, Stefan Feuerriegel
Various deep generative models have been proposed to estimate potential outcomes distributions from observational data. However, none of them have the favorable theoretical propert…