An introduction to causal inference in neuroimaging
published: April 3, 2014, recorded: February 2014, views: 3640
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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.
Download slides: bbci2014_grosse_wentrup_causal_inference_01.pdf (5.9 MB)
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