Applied Bayesian Modeling and Causal Inference From Incomplete-Data Perspectives

Technometrics ◽  
2005 ◽  
Vol 47 (4) ◽  
pp. 519-519
Author(s):  
Keying Ye
2013 ◽  
Vol 11 (4) ◽  
pp. 405-415 ◽  
Author(s):  
Antti Penttinen ◽  
Elena Moltchanova ◽  
Ilkka Nummela

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.


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