"Felix qui potuit rerum cognoscere causas" (Georgics, Virgil 29 BC), Fortunate, who was able to know the hidden causes of things.

More about the history of causality : Master Thesis  of S. Meyahoui (in french)

The project CAUSALI-T-AI is a  PEPR project funded by the ANR (2023-2028). Our consortium, gathering applied mathematicians and computer scientists, aims to tackle the causal modeling challenges using advanced machine learning approaches along four main directions : 

• stable causal modeling, with the objective of unraveling the difficulties resulting from the scarcity of available data and the requirement for identifiability of the learned model 

• learning of causal representations aiming to discover rich latent structures and construct representations that are both useful for machine learning tasks and causally well-founded. 

• causal inference, aiming to adapt causal models from a source domain to target domains, and design interventions to achieve a given goal

• causal learning in uncertain environments

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