4 citations · 9 across the 24 of their papers we have counts for
15 papers · 1 filter
Root cause analysis via difference graph discovery from linear time-series data
Anouk Ruer, Timothée Loranchet, Daria Bystrova +1
Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series throug…
Regret-Based Federated Causal Discovery with Unknown Interventions
Federico Baldo, Charles K. Assaad
Most causal discovery methods recover a completed partially directed acyclic graph representing a Markov equivalence class from observational data. Recent work has extended these m…
Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
Timothée Loranchet, Charles K. Assaad
Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full…
Identifying Macro Causal Effects in C-DMGs over DMGs
Simon Ferreira, Charles K. Assaad
The do-calculus is a sound and complete tool for identifying causal effects in acyclic directed mixed graphs (ADMGs) induced by structural causal models (SCMs). However, in many re…
Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects
Timothée Loranchet, Charles K. Assaad
Identifying controlled direct effects (CDEs) is crucial across numerous scientific domains. While existing methods can identify these effects from causal directed acyclic graphs (D…
Identifying Macro Causal Effects in a C-DMG over ADMGs
Simon Ferreira, Charles K. Assaad
Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods ass…
Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases
Federico Baldo, Simon Ferreira, Charles K. Assaad
Traditional causal discovery methods often depend on strong, untestable assumptions, making them unreliable in real-world applications. In this context, Large Language Models (LLMs…
Causal reasoning in difference graphs
Charles K. Assaad
Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a…
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs
Simon Ferreira, Charles K. Assaad
In this paper, we investigate the identifiability of average controlled direct effects and average natural direct effects in causal systems represented by summary causal graphs, wh…
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
Lei Zan, Charles K. Assaad, Emilie Devijver +2
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 anom…
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…
Inferring extended summary causal graphs from observational time series
Charles K. Assaad, Emilie Devijver, Eric Gaussier
This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for cau…
Entropy-based Discovery of Summary Causal Graphs in Time Series
Charles K. Assaad, Emilie Devijver, Eric Gaussier
This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutua…