27 citations · 63 across the 19 of their papers we have counts for
19 papers
xWhyL: Causal Interactive Learning
Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig +4
Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite…
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)…
STRICTA: Structured Reasoning in Critical Text Assessment for Peer Review and Beyond
Nils Dycke, Matej Zečević, Ilia Kuznetsov +3
Critical text assessment is at the core of many expert activities, such as fact-checking, peer review, and essay grading. Yet, existing work treats critical text assessment as a bl…
SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Harsh Poonia, Moritz Willig, Zhongjie Yu +3
Causal inference in hybrid domains, characterized by a mixture of discrete and continuous variables, presents a formidable challenge. We take a step towards this direction and prop…
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…
Attributions Beyond Neural Networks: The Linear Program Case
Florian Peter Busch, Matej Zečević, Kristian Kersting +1
Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exi…
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation
Jonas Seng, Matej Zečević, Devendra Singh Dhami +1
Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the…
Machines Explaining Linear Programs
David Steinmann, Matej Zečević, Devendra Singh Dhami +1
There has been a recent push in making machine learning models more interpretable so that their performance can be trusted. Although successful, these methods have mostly focused o…
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…
Towards a Solution to Bongard Problems: A Causal Approach
Salahedine Youssef, Matej Zečević, Devendra Singh Dhami +1
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern…
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…
The Causal Loss: Driving Correlation to Imply Causation
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in ma…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…
Structural Causal Models Reveal Confounder Bias in Linear Program Modelling
Matej Zečević, Devendra Singh Dhami, Kristian Kersting
The recent years have been marked by extended research on adversarial attacks, especially on deep neural networks. With this work we intend on posing and investigating the question…
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Matej Zečević, Devendra Singh Dhami, Athresh Karanam +2
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference. As a step towards tractable causal models, we consid…