4 citations · 9 across the 24 of their papers we have counts for
5 papers · 1 filter
Identifiability of total effects from abstractions of time series causal graphs
Charles K. Assaad, Emilie Devijver, Eric Gaussier +2
We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abs…
Case Studies of Causal Discovery from IT Monitoring Time Series
Ali Aït-Bachir, Charles K. Assaad, Christophe de Bignicourt +5
Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their pro…
Identifiability of Direct Effects from Summary Causal Graphs
Simon Ferreira, Charles K. Assaad
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 va…
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
Daria Bystrova, Charles K. Assaad, Julyan Arbel +3
Constraint-based methods and noise-based methods are two distinct families of methods proposed for uncovering causal graphs from observational data. However, both operate under str…
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops
Charles K. Assaad, Imad Ez-zejjari, Lei Zan
This paper presents an approach for identifying the root causes of collective anomalies given observational time series and an acyclic summary causal graph which depicts an abstrac…