156 citations · 235 across the 11 of their papers we have counts for
11 papers
A Unified Approach to Interpretable Causal Root Cause Attribution
Jing Zhou, Dominik Janzing, Sepp Tsang +2
Understanding why a target metric changes is a fundamental problem in data-driven decision making, beyond anomaly detection alone. We study root cause attribution for metric change…
FoundCause: Causal Discovery with Latent Confounders from Observational Data
Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions. We propose FoundCause, an a…
Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum +1
We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pair excluded during learning the ca…
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…
Toward Falsifying Causal Graphs Using a Permutation-Based Test
Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum +2
Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is…
Thompson Sampling with Diffusion Generative Prior
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton +1
In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit fra…
Sequential Kernelized Independence Testing
Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan +1
Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. However, pract…
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki +2
We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimatio…
Quantifying intrinsic causal contributions via structure preserving interventions
Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri +3
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function…
Causal structure based root cause analysis of outliers
Dominik Janzing, Kailash Budhathoki, Lenon Minorics +1
We describe a formal approach to identify 'root causes' of outliers observed in variables in a scenario where the causal relation between the variables is a kno…
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing, Lenon Minorics, Patrick Blöbaum
We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one…