7 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…
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…
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2
Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare. Existi…
ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis
Phi Nguyen Xuan, Nicholas Tagliapietra, Lavdim Halilaj +2
Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptual and methodological comple…
Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis
Nicholas Tagliapietra, Katharina Ensinger, Christoph Zimmer +1
Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics…
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…
Exact Inference for Continuous-Time Gaussian Process Dynamics
Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche +1
Physical systems can often be described via a continuous-time dynamical system. In practice, the true system is often unknown and has to be learned from measurement data. Since dat…