1.3k citations · 3.2k across the 145 of their papers we have counts for
22 papers · 1 filter
Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing
Bernhard Lutz, Marc Adam, Stefan Feuerriegel +2
Fake news on social media has large, negative implications for society. However, little is known about what linguistic cues make people fall for fake news and, hence, how to design…
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
Maresa Schröder, Dennis Frauen, Stefan Feuerriegel
Fairness for machine learning predictions is widely required in practice for legal, ethical, and societal reasons. Existing work typically focuses on settings without unobserved co…
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth +3
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to…
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the…
DSG: An End-to-End Document Structure Generator
Johannes Rausch, Gentiana Rashiti, Maxim Gusev +2
Information in industry, research, and the public sector is widely stored as rendered documents (e.g., PDF files, scans). Hence, to enable downstream tasks, systems are needed that…
Consistent End-to-End Estimation for Counterfactual Fairness
Yuchen Ma, Valentyn Melnychuk, Dennis Frauen +1
Fairness in predictions is of direct importance in practice due to legal, ethical, and societal reasons. This is often accomplished through counterfactual fairness, which ensures t…
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Konstantin Hess, Valentyn Melnychuk, Dennis Frauen +1
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 outcome…
Analyzing User Characteristics of Hate Speech Spreaders on Social Media
Dominique Geissler, Abdurahman Maarouf, Stefan Feuerriegel
Hate speech on social media threatens the mental and physical well-being of individuals and contributes to real-world violence. Resharing is an important driver behind the spread o…
Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang
Document-level relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods for this task use pre-trained language models (LMs)…
Systematic discrepancies in the delivery of political ads on Facebook and Instagram
Dominik Bär, Francesco Pierri, Gianmarco De Francisci Morales +1
Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously u…
Generative AI
Stefan Feuerriegel, Jochen Hartmann, Christian Janiesch +1
The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The…
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
Problem Definition. Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Me…
Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine
Dominique Geissler, Stefan Feuerriegel
The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media. However, the strategy behind the dissemination of propaganda…
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Counterfactual inference aims to answer retrospective "what if" questions and thus belongs to the most fine-grained type of inference in Pearl's causality ladder. Existing methods…
Reliable Off-Policy Learning for Dosage Combinations
Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk +1
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 wor…
Sharp Bounds for Generalized Causal Sensitivity Analysis
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Causal inference from observational data is crucial for many disciplines such as medicine and economics. However, sharp bounds for causal effects under relaxations of the unconfoun…
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Nicolas Banholzer, Thomas Mellan, H Juliette T Unwin +3
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting h…
Addressing distributional shifts in operations management: The case of order fulfillment in customized production
Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic +2
To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lin…
HQP: A Human-Annotated Dataset for Detecting Online Propaganda
Abdurahman Maarouf, Dominik Bär, Dominique Geissler +1
Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using w…
Fair Off-Policy Learning from Observational Data
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons. To achieve this, prior research has contributed various approaches that ensure fairne…
New threats to society from free-speech social media platforms
Dominik Bär, Nicolas Pröllochs, Stefan Feuerriegel
In recent years, several free-speech social media platforms (so-called "alt-techs") have emerged, such as Parler, Gab, and Telegram. These platforms market themselves as alternativ…
Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
Kevin Koch, Martin Maritsch, Eva van Weenen +5
Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially…