12 citations · 20 across the 19 of their papers we have counts for
21 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…
Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting
Morad Laglil, Younes Hlal, Marouane El Hadari +2
Reliable long-horizon time series forecasting is an important yet difficult problem. Trends and seasonality introduce complex temporal structure that challenges learning-based fore…
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
Morad Laglil, Bertrand Pracca, Emilie Devijver +1
Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecast…
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…