7 papers
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…
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…