Bang CW, Witte J, Foraita R, Didelez V. Improving finite sample performance of causal discovery by exploiting temporal structure. Stat Methods Med Res. 2026. → Link
Langbein SH, Krzyziński M, Spytek M, Baniecki H, Biecek P, Wright MN. Interpretable Machine Learning for Survival Analysis. Biom J. 2025;67(6):e70089. → Link
Iqbal K, Intemann T, Börnhorst C, Zhang J, Aleksandrova K. Approaches for harmonization of biomarker data from multiple studies: a narrative methodological review. AJE Adv Res Epidemiol. 2025;1(1):uuaf005. → Link
Andrews RM, Bang CW, Didelez V, Witte J, Foraita R. Software application profile: tpc and micd – R packages for causal discovery with incomplete cohort data. Int J Epidemiol. 2024;53(5):dyae113. 2024 → Link
Foraita R, Witte J, Börnhorst C, Gwozdz W, Pala V, Lissner L, et al. A longitudinal causal graph analysis investigating modifiable risk factors and obesity in a European cohort of children and adolescents. Sci Rep. 2024;14:6822. → Link
Didelez V. Invited commentary: Where do the causal DAGs come from?. Am J Epidemiol. 2024;193(8):1075–1078. → Link
Spytek M, Krzyziński M, Langbein SH, Baniecki H, Wright MN, Biecek P. survex: an R package for explaining machine learning survival models. 2023Bioinformatics. 2023;39(12):btad723. → Link
Preprints
Carmo AS, Abrunhosa Rodrigues L, Peralta AR, Fred ALN, Bentes C, Plácido da Silva H. SeFEF: A Seizure Forecasting Evaluation Framework. [Preprint]. arXiv:2510.11275. 2025. → Link
Book Chapters
Börnhorst C, Wright MN, Didelez V. New analytical approaches to life course epidemiology. In: Lawlor DA, editor. A Life Course Approach to the Epidemiology of Chronic Diseases and Ageing. Oxford: Oxford University Press; 2025. → Link
Conference Papers
Langbein SH, Baniecki H, Fumagalli F, Koenen N, Wright MN, Herbinger J. Functional Decomposition and Shapley Interactions for Interpreting Survival Models. In: Proceedings of the 43rd International Conference on Machine Learning (ICML 2026); Vancouver. [accepted; preprint] → Link
Blesch K, Koenen N, Kapar J, Golchian P, Burk L, Loecher M, Wright MN. Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence; Philadelphia. 2025;39(15):15596–15604. → Link
Bang CW, Didelez V. Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets. In: Proceedings of the 4th Conference on Causal Learning and Reasoning (CLeaR 2025); Lausanne. 2025;275:1116–1146. → Link
Controlled Large Scale Synthetic Motion Dataset Generation Leveraging Text-to-Motion and Sample-wise Quality Assurance. [Submitted; awaiting review]
Langbein SH, Koenen N, Wright MN. Gradient-based Explanations for Deep Learning Survival Models. In: Proceedings of the 42nd International Conference on Machine Learning (ICML 2025); Vancouver. → Link
Richardson A, Putze F. Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques. In: Proceedings of the 27th International Conference on Multimodal Interaction (ICMI ’25); Canberra. → Link
Richardson A, Beetz M, Schultz T, Putze F. CogniFuse and Multimodal Deformers: A Unified Approach for Benchmarking and Modeling Biosignal Fusion. In: 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025). → Link
Splittgerber T. ExLipBaB: Exact Lipschitz Constant Computation for Piecewise Linear Neural Networks. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2026). [accepted; preprint] → Link
Bergen L, Sejdinovic D, Didelez V. The Generalised Kernel Covariance Measure. In: Proceedings of the 5th Conference on Causal Learning and Reasoning (CLeaR 2026); Boston. → Link
Micek C, Warnke L, Abrunhosa Rodrigues L, Putze F, Solovey E. Capturing Team Cognition: A Multimodal Dataset for Adaptive Collaborative Interfaces. In: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems; Barcelona. → Link
Brauße E, Koenen N, Wright MN, Schultz T. Explaining Multimodal Features for Screening of Cognitive Impairment Using Shapley Values. In: 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025). → Link
DeMention: A Modular System for Speech-Based Alzheimer’s Dementia Screening. In: Proceedings of the 8th ACM Conference on Conversational User Interfaces (CUI 2026). [conditionally accepted]
Invited & Public Talks
Bang CW, Witte J, Foraita R, Didelez V. Improving causal discovery with temporal background knowledge. [Talk]. Seminar, Section of Biostatistics, University of Copenhagen; Copenhagen. 2023. → Link
Bang CW, Witte J, Foraita R, Didelez V. Improving causal discovery for cohort data. [Talk]. Autumn Workshop on Causal Machine Learning; Mainz. 2023. → Link
LifespanAI: Vitalwerte, Labor, Radiologie – KI zur Integration von Gesundheitsdaten und Biosignalen. [Talk]. Nacht der Biosignale; Bremen. 2024.
Small Data: KI-Methoden für Health Data [Session]. AI in Health 2024 Symposium; Bremen.
LifespanAI [Session]. AI in Health 2024 Symposium; Bremen.
Bang CW, Witte J, Foraita R, Didelez V. Causal discovery with tiered background knowledge. [Talk]. Workshop on Causal Inference for Time Series (CI4TS) at UAI 2024; Barcelona. → Link
Bergen L, Didelez V. Causal discovery methods for nonlinear mixed-type data: A simulation study. [Talk]. Applied Causal Graphs Workshop 2025; Berlin. 2025. → Link
Bang CW, Witte J, Foraita R, Didelez V. Causal discovery with temporal background knowledge. [Talk]. Two-Day Meeting in Statistics; Copenhagen. 2025. → Link
Bang CW, Witte J, Foraita R, Didelez V. Causal discovery with tiered background knowledge. [Talk]. Workshop on Causal Identification and Discovery, Isaac Newton Institute; Cambridge. → Link
Bergen L, Didelez V. Causal Discovery on Cohort Data with Latent Confounding using Kernel-Based CI Tests [Talk]. Applied Causal Graphs Workshop 2026; Potsdam. 2026. → Link
