1.3k citations · 3.2k across the 145 of their papers we have counts for
22 papers · 1 filter
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…
Generative AI may backfire for counterspeech
Dominik Bär, Abdurahman Maarouf, Stefan Feuerriegel
Online hate speech poses a serious threat to individual well-being and societal cohesion. A promising solution to curb online hate speech is counterspeech. Counterspeech is aimed a…
A qualitative analysis of remote patient monitoring: how a paradox mindset can support balancing emotional tensions in the design of healthcare technologies
Zoe Jonassen, Katharine Lawrence, Batia Mishan Wiesenfeld +2
Remote patient monitoring (RPM) is the use of digital technologies to improve patient care at a distance. However, current RPM solutions are often biased toward tech-savvy patients…
Slowing Down Forgetting in Continual Learning
Pascal Janetzky, Tobias Schlagenhauf, Stefan Feuerriegel
A common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned. In this paper, we propos…
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical…
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…
DiffPO: A causal diffusion model for learning distributions of potential outcomes
Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal +1
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 cau…
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…
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing
Yilmazcan Ozyurt, Stefan Feuerriegel, Mrinmaya Sachan
Knowledge tracing (KT) is a popular approach for modeling students' learning progress over time, which can enable more personalized and adaptive learning. However, existing KT appr…
The Effect of Education in Prompt Engineering: Evidence from Journalists
Amirsiavosh Bashardoust, Yuanjun Feng, Dominique Geissler +2
Large language models (LLMs) are increasingly used in daily work. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs.…
A Fused Large Language Model for Predicting Startup Success
Abdurahman Maarouf, Stefan Feuerriegel, Nicolas Pröllochs
Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. No…
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…
Explainable AI improves task performance in human-AI collaboration
Julian Senoner, Simon Schallmoser, Bernhard Kratzwald +2
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate i…
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar +1
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…
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…
Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
Christof Naumzik, Alice Kongsted, Werner Vach +1
Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by subgrouping, whereby clusters with…
Comparing the willingness to share for human-generated vs. AI-generated fake news
Amirsiavosh Bashardoust, Stefan Feuerriegel, Yash Raj Shrestha
Generative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that use…
Designing Electricity Distribution Networks: The Impact of Demand Coincidence
Gunther Gust, Alexander Schlüter, Stefan Feuerriegel +3
With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. T…
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions
Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel
We propose an algorithm for optimizing the parameters of single hidden layer neural networks. Specifically, we derive a blockwise difference-of-convex (DC) functions representation…
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Milan Kuzmanovic, Dennis Frauen, Tobias Hatt +1
The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by 'leaving no one behind', and, to achieve the SDGs by 2030, poor countries r…