collaborators

10 papers

stat.ML2026

Falsifying Causal Graphs With Outlier Events

William Roy Orchard, Philipp M. Faller, Dominik Janzing

The paper introduces statistical tests that use the propagation of outlier events to falsify candidate causal graphs, providing guarantees on false positives and detection power ev…

stat.ML2026

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…

cs.NI2026

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…

cs.NI2026

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…

cs.CL2026

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…

cs.AI2026

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…