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Just so people know, there is a competing/complementary approach to causality in statistics, called the potential outcomes or (Neyman-)Rubin causal model, which as I understand it is currently more popular than Pearl's graphical/do-calculus approach.

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There is also TS related body of knowledge about causal impact using the notion of counterfactuals. Google has sponsored research in the field [1] and also released an R package [2].

[1] http://research.google.com/pubs/pub41854.html

[2] https://google.github.io/CausalImpact/CausalImpact.html

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