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An introduction to causal inference in neuroimaging
This video was recorded at BBCI Winter School on Neurotechnology, Berlin 2014. A variety of causal inference methods has been introduced to neuroimaging in recent years, including Causal Bayesian Networks, Dynamic Causal Modeling (DCM), Granger Causality, and Linear Non-Gaussian Acyclic Models (LINGAM). While all these methods aim to provide insights into how brain processes interact, they are based on rather different concepts of causality. In this talk, I will review the theoretical foundations of each of these methods, describe their inherent assumptions, and discuss the resulting consequences for the analysis and interpretation of neuroimaging data.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
