Search Results for author:
Found 8 papers, 3 papers with code
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems.
Identifiability of total effects from abstractions of time series causal graphs
We study the problem of identifiability of the total effect of an intervention from observational time series only given an abstraction of the causal graph of the system.
Case Studies of Causal Discovery from IT Monitoring Time Series
Despite its potential benefits, applying causal discovery algorithms on IT monitoring data poses challenges, due to the complexity of the data.
Identifiability of Direct Effects from Summary Causal Graphs
Dynamic structural causal models (SCMs) are a powerful framework for reasoning in dynamic systems about direct effects which measure how a change in one variable affects another variable while holding all other variables constant.
Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series
Constraint-based and noise-based methods have been proposed to discover summary causal graphs from observational time series under strong assumptions which can be violated or impossible to verify in real applications.
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops
Finally, it shows, how the rest of the root causes can be found by comparing direct effects in the normal and in the anomalous regime.
Inferring extended summary causal graphs from observational time series
This study addresses the problem of learning an extended summary causal graph on time series.
Entropy-based Discovery of Summary Causal Graphs in Time Series
This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates.
