3 citations · 4 across the 4 of their papers we have counts for
4 papers
From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
Sujai Hiremath, Dominik Janzing, Philipp Faller +4
Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guar…
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
Michaela Hardt, William R. Orchard, Patrick Blöbaum +2
Identifying root causes for unexpected or undesirable behavior in complex systems is a prevalent challenge. This issue becomes especially crucial in modern cloud applications that…
Beyond Single-Feature Importance with ICECREAM
Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri +1
Which set of features was responsible for a certain output of a machine learning model? Which components caused the failure of a cloud computing application? These are just two exa…
Causal Inference Through the Structural Causal Marginal Problem
Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler +3
We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: gi…