Abstract:
The first section of talk starts with an introduction to causality. We introduce the principle of invariant mechanisms as a key assumption in causal structure learning. We describe the semantics of Structural Causal Models (SCM) and define the concepts of d-separation, Markov conditions, and faithfulness assumption in causal graphical models. In the second part of the talk, two main approaches in causal structure learning are presented: causal learning based on conditional independence tests and learning in additive noise models. Finally, we talk about some recent applications of causal learning in brain research.
Venue:
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM),
Opposite the ARAJ, Artesh Highway, Tehran, Iran on map
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