Causal discovery is of utmost importance for agents who must plan, reason and decide based on observations; where mistaking correlation with causation might lead to unwanted consequences. The gold standard to discover causal relations is to perform experiments. However, experiments are in many cases expensive, unethical, or impossible to realize. In these situations, there is a need for observational causal discovery, that is, the estimation of causal relations from observations alone. We will go through the traditional methods to find causal relationships in observational data: conditional independence, Occam’s razor., among others. Causal discovery in the observational data setting traditionally involves making significant assumptions on the data and on the underlying causal model. This work aims to alleviate some assumptions made on the causal models by exploiting the modularity and expressiveness of neural networks for causal discovery, leveraging both conditional independencies and simplicity of the causal mechanisms.

You can join the meeting online via Webex:
- meeting number: 2740 064 0615
- password: hVwCriMD967.

Join Webex meeting:
https://inria.webex.com/inria-en/j.php?MTID=m6744fb0489e22385e1538c4fef104f61

Or physically in the room 2011 at LISN Campus Universitaire Bât. 660 Claude Shannon - Rue René Thom, 91190 Gif-sur-Yvette.

More information at http://causaltau.github.io