27 citations · 63 across the 19 of their papers we have counts for
8 papers · 1 filter
Causal Explanations Over Time: Articulated Reasoning for Interactive Environments
Sebastian Rödling, Matej Zečević, Devendra Singh Dhami +1
Structural Causal Explanations (SCEs) can be used to automatically generate explanations in natural language to questions about given data that are grounded in a (possibly learned)…
Do Not Marginalize Mechanisms, Rather Consolidate!
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the num…
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami +1
Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why some…
Continual Causal Abstractions
Matej Zečević, Moritz Willig, Jonas Seng +1
This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction t…
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
Matej Zečević, Moritz Willig, Devendra Singh Dhami +1
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in a…
On How AI Needs to Change to Advance the Science of Drug Discovery
Kieran Didi, Matej Zečević
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure…
Can Foundation Models Talk Causality?
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguabl…
Finding Structure and Causality in Linear Programs
Matej Zečević, Florian Peter Busch, Devendra Singh Dhami +1
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure…