1.3k citations · 3.2k across the 145 of their papers we have counts for
27 papers · 1 filter
Analyzing Social Media Claims regarding Youth Online Safety Features to Identify Problem Areas and Communication Gaps
Renkai Ma, Dominique Geissler, Stefan Feuerriegel +3
Social media platforms have faced increasing scrutiny over whether and how they protect youth online. While online risks to children have been well-documented by prior research, ho…
A Meta-Analysis of the Persuasive Power of Large Language Models
Lukas Hölbling, Sebastian Maier, Stefan Feuerriegel
Large language models (LLMs) are increasingly used for persuasion, such as in political communication and marketing, where they affect how people think, choose, and act. Yet, empir…
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…
DeepBlip: Estimating Conditional Average Treatment Effects Over Time
Haorui Ma, Dennis Frauen, Stefan Feuerriegel
Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time. A particular strength of SNMMs is to break the joint effect of treatmen…
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…
Partnering with Generative AI: Experimental Evaluation of Human-Led and Model-Led Interaction in Human-AI Co-Creation
Sebastian Maier, Manuel Schneider, Stefan Feuerriegel
Large language models (LLMs) show strong potential to support creative tasks, but the role of the interface design is poorly understood. In particular, the effect of different mode…
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…
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal +3
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to…
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…
SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
Marie Brockschmidt, Maresa Schröder, Stefan Feuerriegel
Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death. Unlike standard tabular data, surv…
Designing LLMs for cultural sensitivity: Evidence from English-Japanese translation
Helene Tenzer, Oumnia Abidi, Stefan Feuerriegel
Large language models (LLMs) are increasingly used in everyday communication, including multilingual interactions across different cultural contexts. While LLMs can now generate ne…
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Yilmazcan Ozyurt, Tunaberk Almaci, Stefan Feuerriegel +1
We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing ex…
Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment
Dominique Geissler, Claire Robertson, Stefan Feuerriegel
Deepfakes images can erode trust in institutions and compromise election outcomes, as people often struggle to discern real images from deepfake images. Improving digital literacy…
LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
Yuchen Ma, Dennis Frauen, Jonas Schweisthal +1
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 estim…
A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
Yuchen Ma, Jonas Schweisthal, Hengrui Zhang +1
In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make op…
Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek +1
Prior-data fitted networks (PFNs) have recently been proposed as a promising way to train tabular foundation models. PFNs are transformers that are pre-trained on synthetic data ge…
Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis
Niklas Holzner, Sebastian Maier, Stefan Feuerriegel
Generative artificial intelligence (GenAI) is increasingly used to support a wide range of human tasks, yet empirical evidence on its effect on creativity remains scattered. Can Ge…
Treatment Effect Estimation for Optimal Decision-Making
Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal +2
Decision-making across various fields, such as medicine, heavily relies on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether…
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…
PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects
Maresa Schröder, Justin Hartenstein, Stefan Feuerriegel
The average treatment effect (ATE) is widely used to evaluate the effectiveness of drugs and other medical interventions. In safety-critical applications like medicine, reliable in…
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…
Differentially Private Learners for Heterogeneous Treatment Effects
Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel
Patient data is widely used to estimate heterogeneous treatment effects and thus understand the effectiveness and safety of drugs. Yet, patient data includes highly sensitive infor…