5 papers
CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
Nicholas Tagliapietra, Gian Lorenzo Marchioni, Moritz Willig +3
Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiabilit…
Fodor and Pylyshyn's Legacy: Still No Human-like Systematic Compositionality in Neural Networks
Tim Woydt, Moritz Willig, Antonia Wüst +4
Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in pre…
Tagged for Direction: Pinning Down Causal Edge Directions with Precision
Florian Peter Busch, Moritz Willig, Florian Guldan +2
Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type…
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch +3
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping poi…
CausalMan: A physics-based simulator for large-scale causality
Nicholas Tagliapietra, Juergen Luettin, Lavdim Halilaj +3
A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known dat…