4 citations · 9 across the 24 of their papers we have counts for
25 papers
Partitioning Time in Target Trial Emulation
Harold Tankpinou Zoumenou, Simon Ferreira, Charles Assaad +6
In target trial emulation, the treatment strategies that patients follow are inferred from the treatments they actually receive in routine care. However, in most settings, the outc…
Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets
Lei Zan, Charles K. Assaad, Emilie Devijver +1
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxe…
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…
Missing data and cluster graphs: cluster-level missingness vs variable-level missingness
Willow Scott, Eugenio Valdano, Charles Assaad
Missing data is pervasive in many scientific domains such as public health, environmental science, and the social sciences. Recoverability from missing data is typically studied us…
Time Partitioning in Target Trial Emulation
Harold Tankpinou Zoumenou, Simon Ferreira, Charles Assaad +3
In target trial emulation, time partitioning enables researchers to handle time-varying confounders and immortal time bias with appropriate methods. Based on two clinical scenarios…
On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphs
Isabela Belciug, Simon Ferreira, Charles K. Assaad
Observational studies in fields such as epidemiology often rely on covariate adjustment to estimate causal effects. Classical graphical criteria, like the back-door criterion and t…
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…
Identifiability by common backdoor in summary causal graphs of time series
Clément Yvernes, Charles K. Assaad, Emilie Devijver +1
The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed fro…
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…
Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
Simon Ferreira, Charles K. Assaad
Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified cau…
Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion
Charles K. Assaad
Conducting experiments to estimate total effects can be challenging due to cost, ethical concerns, or practical limitations. As an alternative, researchers often rely on causal gra…
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 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…
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…