Find out more about my research below.
I'm a fourth year PhD student at the AIML Lab at TU Darmstadt, Germany, working together with Kristian Kersting and Devendra Singh Dhami, trying to understand how causal reasoning can be integrated and learned by modern LLM and related AI systems. My research focuses on building AI models that are able to reflect and reason over causal information in textual and visual data. I'm furthermore interested in how causal systems can evolve and change. In my recent work I formalized meta-causal models which are able to model the dynamic emergence and vanishing of causal relations under the assumption of underlying mediation processes.
If you find any of my works intersting or simply would like to discuss, feel free to contact me!
Here are some topics I'm currently thinking about.
Find my publications on Google Scholar.
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, and Kristian Kersting.
Systems with switching causal relations: A meta-causal perspective.
NeurIPS2024 Causal Representation Learning Workshop, 2024. Link: OpenReview.
Florian Peter Busch, Moritz Willig, Jonas Seng, Kristian Kersting, and Devendra Singh Dhami.
psiNet: Efficient causal modeling at scale.
International Conference on Probabilistic Graphical Models (PGM), pages 452–469. PMLR, 2024. Link: PMLR.
Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Zečević, Kristian Kersting, and Devendra Singh Dhami.
chiSPN: Characteristic interventional sum-product networks for causal inference in hybrid domains.
The 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024. Link: OpenReview.
Moritz Willig, Matej Zečević, and Kristian Kersting.
“Do not disturb my circles!” Identifying the type of counterfactual at hand.
Conference on Advances in Robust Argumentation Machines (RATIO), pages 266–275. Springer, 2024. Link: Springer.
Anita Keshmirian, Moritz Willig, Babak Hemmatian, Kristian Kersting, Ulrike Hahn, and Tobias
Gerstenberg.
Chain versus common cause: Biased causal strength judgments in humans and large language models.
Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), volume 46, 2024.
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting.
Do not marginalize mechanisms, rather consolidate!
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023. Link: OpenReview.
Matej Zečević*, Moritz Willig*, Devendra Singh Dhami, and Kristian Kersting.
Causal parrots: Large language models may talk causality but are not causal.
Transactions on Machine Learning Research (TMLR), 2023. Link: OpenReview.
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting.
Identifying challenges for generalizing to the pearl causal hierarchy on images.
ICLR 2023 Workshop on Domain Generalization,
2023. Link: OpenReview.
Moritz Willig*, Matej Zečević*, Devendra Singh Dhami, and Kristian Kersting.
Can foundation models talk causality?
UAI 2022 Workshop on Causal Representation Learning, 2022. Link: ArXiv.
Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, and Johannes Fürnkranz.
Learning to play the chess variant crazyhouse above world champion level with deep neural networks and human data.
Frontiers in Artificial Intelligence, 3:24, 2020. Link: FrontiersIn.
