Recently, I ran across several papers and online resources that mention Granger causality. Brief browsing through the corresponding Wikipedia article left me with the impression that this term refers to causality in the context of time series (or, more generally, stochastic processes). Moreover, reading this nice blog post created an additional confusion in how to view this approach.
I'm by no means a person knowledgeable about causality, as my fuzzy understanding of the concept consists of partly common sense, common knowledge, some exposure to latent variable modeling and structural equation modeling (SEM) and reading a bit from Judea Pearl's work on causality - not THE book of his, but more along the lines of an interesting overview paper by Pearl (2009), which for some reason, surprisingly, doesn't mention Granger causality at all.
In this context, I'm wondering about whether Granger causality is something more general than a time series (stochastic) framework and, if such, what is its relation (commonalities and differences) to Pearl's causality framework, based on the structural causal model (SCM), which, as far as I understand, is, in turn, based on direct acyclic graphs (DAGs) and counterfactuals. It seems that Granger causality can be classified as a general approach to causal inference for dynamic systems, considering the existence of dynamic causal modeling (DCM) approach (Chicharro & Panzeri, 2014). However, my concern is about whether (and, if so, how) it is possible to compare the two approaches, one of which is based on stochastic process analysis and the other is not.
More generally, what do you think would be a sensible high-level approach - if one is possible - for considering all currently existing causality theories within a single comprehensive causality framework (as different perspectives)? This question is largely triggered by my attempt to read an excellent and comprehensive paper by Chicharro and Panzeri (2014) as well as reviewing an interesting causal inference course at University of California, Berkeley (Petersen & Balzer, 2014) .
Chicharro, D., & Panzeri, S. (2014). Algorithms of causal inference for the analysis of effective connectivity among brain regions. Frontiers in Neuroinformatics, 8(64). doi: 10.3389/fninf.2014.00064 Retrieved from http://journal.frontiersin.org/article/10.3389/fninf.2014.00064/pdf
Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146. doi:10.1214/09-SS057 Retrieved from http://projecteuclid.org/download/pdfview_1/euclid.ssu/1255440554
Petersen, M., & Balzer, L. (2014). Introduction to causal inference. University of California, Berkeley. [Website] Retrieved from http://www.ucbbiostat.com