4 citations · 4 across the 13 of their papers we have counts for
16 papers
Inferring Causal Relations between Two Sequences of Events with Language Models
Nishchal Prasad, Eric Gaussier, Emilie Devijver +3
Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlations. Causal discovery aims to…
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…
Cluster emission and its impact on the r-process nucleosynthesis
Natalia Thomas, Silvia Bara, Thomas Elias Cocolios +20
Cluster emission is an exotic decay mode between alpha-decay and fission, in which a parent nucleus emits a cluster of nucleons heavier than an alpha-particle, but lighter than wha…
Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Clément Yvernes, Emilie Devijver, Marianne Clausel +1
The do-calculus defines a general system of inference for interventional queries, allowing causal quantities to be transformed through successive applications of its rules. This pr…
Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
Clément Yvernes, Emilie Devijver, Adèle H. Ribeiro +2
Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependenc…
Identifiability in Causal Abstractions: A Hierarchy of Criteria
Clément Yvernes, Emilie Devijver, Marianne Clausel +1
Identifying the effect of a treatment from observational data typically requires assuming a fully specified causal diagram. However, such diagrams are rarely known in practice, esp…
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…
Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
Clément Yvernes, Emilie Devijver, Eric Gaussier
The identifiability problem for interventions aims at assessing whether the total causal effect can be written with a do-free formula, and thus be estimated from observational data…
Ensembles of Probabilistic Regression Trees
Alexandre Seiller, Éric Gaussier, Emilie Devijver +2
Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applic…
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…
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…
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…
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Myriam Tami, Marianne Clausel, Emilie Devijver +4
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these…