1 citations · 1 across the 11 of their papers we have counts for
12 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…
Falsifying Causal Graphs With Outlier Events
William Roy Orchard, Philipp M. Faller, Dominik Janzing
True causal relationships are rarely known, and inferring causal graphs from data is hard. A fundamental challenge is how to assess whether a given causal graph is good in the abse…
NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
Fabien Chraim, Jian Zhang, Dominik Janzing +3
Can a learned model capture how faults propagate through a large-scale network and use this knowledge to causally attribute customer impact to its underlying root cause? Existing r…
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
Fabien Chraim, Dominik Janzing, John Evans
Cloud-computing relies on large-scale networks which are inherently complex systems. In this paper, we present a novel approach to root cause analysis (RCA) of cloud network incide…
Measuring Semantic Progress in Multi-turn Dialogue via Information Gain
Paul He, Shiva Kasiviswanathan, Dominik Janzing
Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses. We focus on a key dimension of information-seeking dialo…
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
Erik Jahn, Dominik Janzing
For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evaluating collections of $\binom…
Formalizing and falsifying causal pathways of rare events
Anahita Haghighat, Dominik Janzing
Building on recent formalizations of root cause analysis for rare events (``outliers'') in structural equation models, we propose a formal definition of a causal pathway and discus…
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…
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
Daniela Schkoda, Dominik Janzing
We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible t…
Causal vs. Anticausal merging of predictors
Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf +1
We study the differences arising from merging predictors in the causal and anticausal directions using the same data. In particular we study the asymmetries that arise in a simple…
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…
Root Cause Analysis of Outliers with Missing Structural Knowledge
William Roy Orchard, Nastaran Okati, Sergio Hernan Garrido Mejia +2
The goal of Root Cause Analysis (RCA) is to explain why an anomaly occurred by identifying where the fault originated. Several recent works model the anomalous event as resulting f…