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.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM),
Opposite the ARAJ, Artesh Highway, Tehran, Iran on map
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