Clearly the X makes it go faster , just like the flame decals on my bike . <| sep |> umm , not when there ' s like 15 seconds left <| sep |> Donald Trump is going to sink the ship of state . <| sep |> scrap for armor ? don ' t think so <| sep |> Yes . Probably longer than you have . Either way , it has no bearing on the fact that it ' s shoehorned in . <| sep |> Too many accessories looks tacky . Saw a dude wearing a rolex , ray bans , a cross pendant and earrings and it just looked like shit <| sep |> After reading the comments on Gelman ' s blog as well as Pearl ' s paper on Bayesianism and Causality , I ' m still not sure what enlightened Bayesianism is . It might have something to do with Pearl ' s docalculus , which is new to me and does appear to be something new in the math . However it makes sense that an attempt would be made to provide a formal definition of causality , since Pearl is of course correct that probability theory , as it currently stands , is insufficiently expressive to completely capture causality . Bayesian hierarchical models can capture causality to some extent , but only certain aspects of it and often indirectly . Furthermore , in probability theory one is still confronted with the fact thatCausality does not imply dependence . The canonical example being two independent unbiased binary random variables A and B , and an associated XOR function CA C Bwith tableA 0 , B 0 C 0 A 0 , B 1 C 1 A 1 , B 0 C 1 A 1 , B 1 C 0 Clearly A and B together have a causal relationship with C , since knowing both A and B completely determines C . However PCA PCB PC , and thus knowing just one of A or B provides you with no new information about C , despite there being a clear causal relationship between A and C and between B and C . As an aside , has anyone learned about Pearl ' s docalculus ? Is it a framework worth learning ?